hypothesis characteristics traits

5 Characteristics of a Good Hypothesis: A Guide for Researchers

• by Brian Thomas
• October 10, 2023

Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.

Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!

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5 Characteristics of a Good Hypothesis

Clear and specific.

A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!

Testable and Falsifiable

A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.

Based on Existing Knowledge

Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!

Specific Predictions

No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!

Relevant to the Research Question

A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!

And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!

FAQs: Characteristics of a Good Hypothesis

In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!

What Are Two Important Characteristics of a Good Hypothesis

A good hypothesis possesses two important characteristics:

Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.

Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.

What Are the Types of Hypothesis in Research

In research, there are three main types of hypotheses:

Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.

Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.

Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”

Can a Hypothesis Be Proven True

In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.

What Are the Six Parts of a Hypothesis

A hypothesis typically consists of six essential parts:

Research Question : A clear and concise question that the hypothesis seeks to answer.

Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.

Population : The specific group or individuals the hypothesis is concerned with.

Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”

Predictability : A statement of the predicted outcome or result based on the relationship between variables.

Testability : The ability to design an experiment or gather data to support or reject the hypothesis.

How Do You Start a Hypothesis Sentence

When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:

• If [independent variable], then [dependent variable] because [explanation of expected relationship].

This structure allows for a straightforward and logical formulation of the hypothesis.

What Are Examples of Hypotheses

Here are a few examples of well-formulated hypotheses:

If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.

If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.

If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.

What Are the Five Key Elements to a Good Hypothesis

A good hypothesis should include the following five key elements:

Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.

Testability : It should be possible to test the hypothesis through experimentation or data collection.

Relevance : The hypothesis should be directly tied to the research question or problem being investigated.

Specificity : It must clearly state the relationship or difference between variables being studied.

Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.

What Makes a Good Hypothesis in a Research Paper

In a research paper, a good hypothesis should have the following characteristics:

Relevance : It must directly relate to the research topic and address the objectives of the study.

Clarity : The hypothesis should be concise and precisely worded to avoid confusion.

Unambiguous : It must leave no room for multiple interpretations or ambiguity.

Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.

Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.

Is a Hypothesis Always a Question

No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.

What Are the Three Things Needed for a Good Hypothesis

For a hypothesis to be considered good, it must fulfill the following three criteria:

Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.

Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.

Relevance : The hypothesis should directly address the research question or problem being investigated.

What Are the Four Components to a Good Hypothesis

A good hypothesis typically consists of four components:

Independent Variable : The variable being manipulated or controlled by the researcher.

Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.

Directionality : The predicted relationship or difference between the independent and dependent variables.

Population : The specific group or individuals to which the hypothesis applies.

How Do You Formulate a Hypothesis

To formulate a hypothesis, follow these steps:

Identify the Research Topic : Clearly define the area or phenomenon you want to study.

Conduct Background Research : Review existing literature and research to gain knowledge about the topic.

Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.

State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.

Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.

Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.

What Is a Characteristic of a Hypothesis MCQ

Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.

What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific

For a hypothesis to be considered scientific, it must satisfy the following five criteria:

Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.

Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.

Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.

Relevance : It must directly address the research question or problem being investigated.

Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.

What Are the Steps of Theory Development in Scientific Methods

In scientific methods, theory development typically involves the following steps:

Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.

Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.

Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.

Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.

Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.

Which of the Following Makes a Good Hypothesis

A good hypothesis is characterized by:

Testability : The ability to form experiments or gather data to support or refute the hypothesis.

Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.

Clarity : A clear and concise statement or question that leaves no room for ambiguity.

Relevancy : Directly addressing the research question or problem at hand.

Remember, it is important to select the option that encompasses all these characteristics.

What Are the Characteristics of a Good Hypothesis

A good hypothesis possesses several characteristics, such as:

Testability : It should allow for empirical testing through experiments or data collection.

Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.

Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.

Relevance : The hypothesis should directly relate to the research question or problem being investigated.

What Is the Five-Step p-value Approach to Hypothesis Testing

The five-step p-value approach is a commonly used framework for hypothesis testing:

Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.

Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).

Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.

Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.

Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.

What Are the Stages of Hypothesis

The stages of hypothesis generally include:

Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.

Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.

Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.

Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.

Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.

What Is a Characteristic of a Good Hypothesis

A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.

How Do You Write a Good Hypothesis Example

To write a good hypothesis example, follow these guidelines:

If possible, use the “If…then…” format to express a conditional relationship between variables.

Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.

Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.

For instance, consider the following example:

If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.

What Is the Difference Between Hypothesis and Hypotheses

The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.

What Is a Good Hypothesis Statement

A good hypothesis statement exhibits the following qualities:

Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.

Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.

Specificity : It must clearly state the predicted relationship or difference between variables.

By adhering to these criteria, a good hypothesis statement guides research efforts effectively.

What Is Not a Characteristic of a Good Hypothesis

A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.

By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,

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Brian Thomas

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Characteristics Of A Good Hypothesis​

What exactly is a hypothesis.

A hypothesis is a conclusion reached after considering the evidence. This is the first step in any investigation, where the research questions are translated into a prediction. Variables, population, and the relationship between the variables are all included. A research hypothesis is a hypothesis that is tested to see if two or more variables have a relationship. Now let’s have a look at the characteristics of a  good hypothesis.

 Characteristics of

A good hypothesis has the following characteristics.

 Ability To Predict

Closest to things that can be seen, testability, relevant to the issue, techniques that are applicable, new discoveries have been made as a result of this ., harmony & consistency.

• The similarity between the two phenomena.
• Observations from previous studies, current experiences, and feedback from rivals.
• Theories based on science.
• People’s thinking processes are influenced by general patterns.
• A straightforward hypothesis
• Complex Hypothesis
• Hypothesis  with a certain direction
•  Non-direction Hypothesis
• Null Hypothesis
• Hypothesis of association and chance

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

Business Ethics

( Click on Topic to Read )

• What is Ethics?
• What is Business Ethics?
• Values, Norms, Beliefs and Standards in Business Ethics
• Indian Ethos in Management
• Ethical Issues in Marketing
• Ethical Issues in HRM
• Ethical Issues in IT
• Ethical Issues in Production and Operations Management
• Ethical Issues in Finance and Accounting
• What is Corporate Governance?
• What is Ownership Concentration?
• What is Ownership Composition?
• Types of Companies in India
• Internal Corporate Governance
• External Corporate Governance
• Corporate Governance in India
• What is Enterprise Risk Management (ERM)?
• What is Assessment of Risk?
• What is Risk Register?
• Risk Management Committee

Corporate social responsibility (CSR)

• Theories of CSR
• Arguments Against CSR
• Business Case for CSR
• Importance of CSR in India
• Drivers of Corporate Social Responsibility
• Developing a CSR Strategy
• Implement CSR Commitments
• CSR Marketplace
• CSR at Workplace
• Environmental CSR
• CSR with Communities and in Supply Chain
• Community Interventions
• CSR Monitoring
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What Are the Elements of a Good Hypothesis?

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A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable . While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.

Cause and Effect or 'If, Then' Relationships

A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:

If you increase the duration of light, (then) corn plants will grow more each day.

The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment . The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.

Key Points of Hypothesis

When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.

• Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
• Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
• Would your experiment be safe and ethical?
• Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.

What If the Hypothesis Is Incorrect?

It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.

For example, the hypothesis:

The rate of corn plant growth does not depend on the duration of light.

This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.

Need more examples of how to write a hypothesis ? Here you go:

• If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
• If you drop different objects, they will fall at the same rate.
• If you eat only fast food, then you will gain weight.
• If you use cruise control, then your car will get better gas mileage.
• If you apply a top coat, then your manicure will last longer.
• If you turn the lights on and off rapidly, then the bulb will burn out faster.
• Null Hypothesis Definition and Examples
• Six Steps of the Scientific Method
• What Is a Hypothesis? (Science)
• Understanding Simple vs Controlled Experiments
• The Role of a Controlled Variable in an Experiment
• Dependent Variable Definition and Examples
• How To Design a Science Fair Experiment
• Null Hypothesis Examples
• Independent Variable Definition and Examples
• Scientific Method Vocabulary Terms
• Scientific Method Flow Chart
• Examples of Independent and Dependent Variables
• Definition of a Hypothesis
• Scientific Variable
• What Is an Experiment? Definition and Design

Choose Your Test

Sat / act prep online guides and tips, what is a hypothesis and how do i write one.

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Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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Gregor Mendel and the Principles of Inheritance

Traits are passed down in families in different patterns. Pedigrees can illustrate these patterns by following the history of specific characteristics, or phenotypes, as they appear in a family. For example, the pedigree in Figure 1 shows a family in which a grandmother (generation I) has passed down a characteristic (shown in solid red) through the family tree. The inheritance pattern of this characteristic is considered dominant , because it is observable in every generation. Thus, every individual who carries the genetic code for this characteristic will show evidence of the characteristic. In contrast, Figure 2 shows a different pattern of inheritance, in which a characteristic disappears in one generation, only to reappear in a subsequent one. This pattern of inheritance, in which the parents do not show the phenotype but some of the children do, is considered recessive . But where did our knowledge of dominance and recessivity first come from?

Gregor Mendel’s Courage and Persistence

Mendel was curious about how traits were transferred from one generation to the next, so he set out to understand the principles of heredity in the mid-1860s. Peas were a good model system, because he could easily control their fertilization by transferring pollen with a small paintbrush. This pollen could come from the same flower (self-fertilization), or it could come from another plant's flowers (cross-fertilization). First, Mendel observed plant forms and their offspring for two years as they self-fertilized, or "selfed," and ensured that their outward, measurable characteristics remained constant in each generation. During this time, Mendel observed seven different characteristics in the pea plants, and each of these characteristics had two forms (Figure 3). The characteristics included height (tall or short), pod shape (inflated or constricted), seed shape (smooth or winkled), pea color (green or yellow), and so on. In the years Mendel spent letting the plants self, he verified the purity of his plants by confirming, for example, that tall plants had only tall children and grandchildren and so forth. Because the seven pea plant characteristics tracked by Mendel were consistent in generation after generation of self-fertilization, these parental lines of peas could be considered pure-breeders (or, in modern terminology, homozygous for the traits of interest). Mendel and his assistants eventually developed 22 varieties of pea plants with combinations of these consistent characteristics.

Mendel not only crossed pure-breeding parents, but he also crossed hybrid generations and crossed the hybrid progeny back to both parental lines. These crosses (which, in modern terminology, are referred to as F 1 , F 1 reciprocal, F 2 , B 1 , and B 2 ) are the classic crosses to generate genetically hybrid generations.

Understanding Dominant Traits

Understanding recessive traits.

When conducting his experiments, Mendel designated the two pure-breeding parental generations involved in a particular cross as P 1 and P 2 , and he then denoted the progeny resulting from the crossing as the filial, or F 1 , generation. Although the plants of the F 1 generation looked like one parent of the P generation, they were actually hybrids of two different parent plants. Upon observing the uniformity of the F 1 generation, Mendel wondered whether the F 1 generation could still possess the nondominant traits of the other parent in some hidden way.

To understand whether traits were hidden in the F 1 generation, Mendel returned to the method of self-fertilization. Here, he created an F 2 generation by letting an F 1 pea plant self-fertilize (F 1 x F 1 ). This way, he knew he was crossing two plants of the exact same genotype . This technique, which involves looking at a single trait, is today called a monohybrid cross . The resulting F 2 generation had seeds that were either round or wrinkled. Figure 4 shows an example of Mendel's data.

When looking at the figure, notice that for each F 1 plant, the self-fertilization resulted in more round than wrinkled seeds among the F 2 progeny. These results illustrate several important aspects of scientific data:

• Multiple trials are necessary to see patterns in experimental data.
• There is a lot of variation in the measurements of one experiment.
• A large sample size, or "N," is required to make any quantitative comparisons or conclusions.

In Figure 4, the result of Experiment 1 shows that the single characteristic of seed shape was expressed in two different forms in the F 2 generation: either round or wrinkled. Also, when Mendel averaged the relative proportion of round and wrinkled seeds across all F 2 progeny sets, he found that round was consistently three times more frequent than wrinkled. This 3:1 proportion resulting from F 1 x F 1 crosses suggested there was a hidden recessive form of the trait. Mendel recognized that this recessive trait was carried down to the F 2 generation from the earlier P generation .

Mendel and Alleles

As mentioned, Mendel's data did not support the ideas about trait blending that were popular among the biologists of his time. As there were never any semi-wrinkled seeds or greenish-yellow seeds, for example, in the F 2 generation, Mendel concluded that blending should not be the expected outcome of parental trait combinations. Mendel instead hypothesized that each parent contributes some particulate matter to the offspring. He called this heritable substance "elementen." (Remember, in 1865, Mendel did not know about DNA or genes.) Indeed, for each of the traits he examined, Mendel focused on how the elementen that determined that trait was distributed among progeny. We now know that a single gene controls seed form, while another controls color, and so on, and that elementen is actually the assembly of physical genes located on chromosomes. Multiple forms of those genes, known as alleles , represent the different traits. For example, one allele results in round seeds, and another allele specifies wrinkled seeds.

One of the most impressive things about Mendel's thinking lies in the notation that he used to represent his data. Mendel's notation of a capital and a lowercase letter ( Aa ) for the hybrid genotype actually represented what we now know as the two alleles of one gene : A and a . Moreover, as previously mentioned, in all cases, Mendel saw approximately a 3:1 ratio of one phenotype to another. When one parent carried all the dominant traits ( AA ), the F 1 hybrids were "indistinguishable" from that parent. However, even though these F 1 plants had the same phenotype as the dominant P 1 parents, they possessed a hybrid genotype ( Aa ) that carried the potential to look like the recessive P 1 parent ( aa ). After observing this potential to express a trait without showing the phenotype, Mendel put forth his second principle of inheritance: the principle of segregation . According to this principle, the "particles" (or alleles as we now know them) that determine traits are separated into gametes during meiosis , and meiosis produces equal numbers of egg or sperm cells that contain each allele (Figure 5).

Dihybrid Crosses

Mendel had thus determined what happens when two plants that are hybrid for one trait are crossed with each other, but he also wanted to determine what happens when two plants that are each hybrid for two traits are crossed. Mendel therefore decided to examine the inheritance of two characteristics at once. Based on the concept of segregation , he predicted that traits must sort into gametes separately. By extrapolating from his earlier data, Mendel also predicted that the inheritance of one characteristic did not affect the inheritance of a different characteristic.

Mendel tested this idea of trait independence with more complex crosses. First, he generated plants that were purebred for two characteristics, such as seed color (yellow and green) and seed shape (round and wrinkled). These plants would serve as the P 1 generation for the experiment. In this case, Mendel crossed the plants with wrinkled and yellow seeds ( rrYY ) with plants with round, green seeds ( RRyy ). From his earlier monohybrid crosses, Mendel knew which traits were dominant: round and yellow. So, in the F 1 generation, he expected all round, yellow seeds from crossing these purebred varieties, and that is exactly what he observed. Mendel knew that each of the F 1 progeny were dihybrids; in other words, they contained both alleles for each characteristic ( RrYy ). He then crossed individual F 1 plants (with genotypes RrYy ) with one another. This is called a dihybrid cross . Mendel's results from this cross were as follows:

• 315 plants with round, yellow seeds
• 108 plants with round, green seeds
• 101 plants with wrinkled, yellow seeds
• 32 plants with wrinkled, green seeds

Thus, the various phenotypes were present in a 9:3:3:1 ratio (Figure 6).

Next, Mendel went through his data and examined each characteristic separately. He compared the total numbers of round versus wrinkled and yellow versus green peas, as shown in Tables 1 and 2.

Table 1: Data Regarding Seed Shape

Table 2: Data Regarding Pea Color

The proportion of each trait was still approximately 3:1 for both seed shape and seed color. In other words, the resulting seed shape and seed color looked as if they had come from two parallel monohybrid crosses; even though two characteristics were involved in one cross, these traits behaved as though they had segregated independently. From these data, Mendel developed the third principle of inheritance: the principle of independent assortment . According to this principle, alleles at one locus segregate into gametes independently of alleles at other loci. Such gametes are formed in equal frequencies.

Mendel’s Legacy

More lasting than the pea data Mendel presented in 1862 has been his methodical hypothesis testing and careful application of mathematical models to the study of biological inheritance. From his first experiments with monohybrid crosses, Mendel formed statistical predictions about trait inheritance that he could test with more complex experiments of dihybrid and even trihybrid crosses. This method of developing statistical expectations about inheritance data is one of the most significant contributions Mendel made to biology.

But do all organisms pass their on genes in the same way as the garden pea plant? The answer to that question is no, but many organisms do indeed show inheritance patterns similar to the seminal ones described by Mendel in the pea. In fact, the three principles of inheritance that Mendel laid out have had far greater impact than his original data from pea plant manipulations. To this day, scientists use Mendel's principles to explain the most basic phenomena of inheritance.

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Characteristics & Qualities of a Good Hypothesis

A good hypothesis possesses the following certain attributes.

Power of Prediction

One of the valuable attribute of a good hypothesis is to predict for future. It not only clears the present problematic situation but also predict for the future that what would be happened in the coming time. So, hypothesis is a best guide of research activity due to power of prediction.

Closest to observable things

A hypothesis must have close contact with observable things. It does not believe on air castles but it is based on observation. Those things and objects which we cannot observe, for that hypothesis cannot be formulated. The verification of a hypothesis is based on observable things.

A hypothesis should be so dabble to every layman, P.V young says, “A hypothesis wo0uld be simple, if a researcher has more in sight towards the problem”. W-ocean stated that, “A hypothesis should be as sharp as razor’s blade”. So, a good hypothesis must be simple and have no complexity.

A hypothesis must be conceptually clear. It should be clear from ambiguous information’s. The terminology used in it must be clear and acceptable to everyone.

Testability

A good hypothesis should be tested empirically. It should be stated and formulated after verification and deep observation. Thus testability is the primary feature of a good hypothesis.

Relevant to Problem

If a hypothesis is relevant to a particular problem, it would be considered as good one. A hypothesis is guidance for the identification and solution of the problem, so it must be accordance to the problem.

It should be formulated for a particular and specific problem. It should not include generalization. If generalization exists, then a hypothesis cannot reach to the correct conclusions.

Relevant to available Techniques

Hypothesis must be relevant to the techniques which is available for testing. A researcher must know about the workable techniques before formulating a hypothesis.

Fruitful for new Discoveries

It should be able to provide new suggestions and ways of knowledge. It must create new discoveries of knowledge J.S. Mill, one of the eminent researcher says that “Hypothesis is the best source of new knowledge it creates new ways of discoveries”.

Consistency & Harmony

Internal harmony and consistency is a major characteristic of good hypothesis. It should be out of contradictions and conflicts. There must be a close relationship between variables which one is dependent on other.

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What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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11.1: Personality Traits

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University of Utah, University of Virginia, Michigan State University

Personality traits reflect people’s characteristic patterns of thoughts, feelings, and behaviors. Personality traits imply consistency and stability—someone who scores high on a specific trait like Extraversion is expected to be sociable in different situations and over time. Thus, trait psychology rests on the idea that people differ from one another in terms of where they stand on a set of basic trait dimensions that persist over time and across situations. The most widely used system of traits is called the Five-Factor Model. This system includes five broad traits that can be remembered with the acronym OCEAN: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Each of the major traits from the Big Five can be divided into facets to give a more fine-grained analysis of someone's personality. In addition, some trait theorists argue that there are other traits that cannot be completely captured by the Five-Factor Model. Critics of the trait concept argue that people do not act consistently from one situation to the next and that people are very influenced by situational forces. Thus, one major debate in the field concerns the relative power of people’s traits versus the situations in which they find themselves as predictors of their behavior.

learning objectives

• List and describe the “Big Five” (“OCEAN”) personality traits that comprise the Five-Factor Model of personality.
• Describe how the facet approach extends broad personality traits.
• Explain a critique of the personality-trait concept.
• Describe in what ways personality traits may be manifested in everyday behavior.
• Describe each of the Big Five personality traits, and the low and high end of the dimension.
• Give examples of each of the Big Five personality traits, including both a low and high example.
• Describe how traits and social learning combine to predict your social activities.
• Describe your theory of how personality traits get refined by social learning.

Introduction

When we observe people around us, one of the first things that strikes us is how different people are from one another. Some people are very talkative while others are very quiet. Some are active whereas others are couch potatoes. Some worry a lot, others almost never seem anxious. Each time we use one of these words, words like “talkative,” “quiet,” “active,” or “anxious,” to describe those around us, we are talking about a person’s personality — the characteristic ways that people differ from one another. Personality psychologists try to describe and understand these differences.

Although there are many ways to think about the personalities that people have, Gordon Allport and other “personologists” claimed that we can best understand the differences between individuals by understanding their personality traits. Personality traits reflect basic dimensions on which people differ (Matthews, Deary, & Whiteman, 2003). According to trait psychologists, there are a limited number of these dimensions (dimensions like Extraversion, Conscientiousness, or Agreeableness), and each individual falls somewhere on each dimension, meaning that they could be low, medium, or high on any specific trait.

An important feature of personality traits is that they reflect continuous distributions rather than distinct personality types. This means that when personality psychologists talk about Introverts and Extraverts, they are not really talking about two distinct types of people who are completely and qualitatively different from one another. Instead, they are talking about people who score relatively low or relatively high along a continuous distribution. In fact, when personality psychologists measure traits like Extraversion , they typically find that most people score somewhere in the middle, with smaller numbers showing more extreme levels. The figure below shows the distribution of Extraversion scores from a survey of thousands of people. As you can see, most people report being moderately, but not extremely, extraverted, with fewer people reporting very high or very low scores.

There are three criteria that are characterize personality traits: (1) consistency, (2) stability, and (3) individual differences.

• To have a personality trait, individuals must be somewhat consistent across situations in their behaviors related to the trait. For example, if they are talkative at home, they tend also to be talkative at work.
• Individuals with a trait are also somewhat stable over time in behaviors related to the trait. If they are talkative, for example, at age 30, they will also tend to be talkative at age 40.
• People differ from one another on behaviors related to the trait. Using speech is not a personality trait and neither is walking on two feet—virtually all individuals do these activities, and there are almost no individual differences. But people differ on how frequently they talk and how active they are, and thus personality traits such as Talkativeness and Activity Level do exist.

A challenge of the trait approach was to discover the major traits on which all people differ. Scientists for many decades generated hundreds of new traits, so that it was soon difficult to keep track and make sense of them. For instance, one psychologist might focus on individual differences in “friendliness,” whereas another might focus on the highly related concept of “sociability.” Scientists began seeking ways to reduce the number of traits in some systematic way and to discover the basic traits that describe most of the differences between people.

The way that Gordon Allport and his colleague Henry Odbert approached this was to search the dictionary for all descriptors of personality (Allport & Odbert, 1936). Their approach was guided by the lexical hypothesis , which states that all important personality characteristics should be reflected in the language that we use to describe other people. Therefore, if we want to understand the fundamental ways in which people differ from one another, we can turn to the words that people use to describe one another. So if we want to know what words people use to describe one another, where should we look? Allport and Odbert looked in the most obvious place—the dictionary. Specifically, they took all the personality descriptors that they could find in the dictionary (they started with almost 18,000 words but quickly reduced that list to a more manageable number) and then used statistical techniques to determine which words “went together.” In other words, if everyone who said that they were “friendly” also said that they were “sociable,” then this might mean that personality psychologists would only need a single trait to capture individual differences in these characteristics. Statistical techniques were used to determine whether a small number of dimensions might underlie all of the thousands of words we use to describe people.

The Five-Factor Model of Personality

Research that used the lexical approach showed that many of the personality descriptors found in the dictionary do indeed overlap. In other words, many of the words that we use to describe people are synonyms. Thus, if we want to know what a person is like, we do not necessarily need to ask how sociable they are, how friendly they are, and how gregarious they are. Instead, because sociable people tend to be friendly and gregarious, we can summarize this personality dimension with a single term. Someone who is sociable, friendly, and gregarious would typically be described as an “Extravert.” Once we know she is an extravert, we can assume that she is sociable, friendly, and gregarious.

Statistical methods (specifically, a technique called factor analysis ) helped to determine whether a small number of dimensions underlie the diversity of words that people like Allport and Odbert identified. The most widely accepted system to emerge from this approach was “The Big Five” or “ Five-Factor Model ” (Goldberg, 1990; McCrae & John, 1992; McCrae & Costa, 1987). The Big Five comprises five major traits shown in the Figure 3.2.2 below. A way to remember these five is with the acronym OCEAN (O is for Openness ; C is for Conscientiousness ; E is for Extraversion ; A is for Agreeableness ; N is for Neuroticism ). Figure 3.2.3 provides descriptions of people who would score high and low on each of these traits.

Scores on the Big Five traits are mostly independent. That means that a person’s standing on one trait tells very little about their standing on the other traits of the Big Five. For example, a person can be extremely high in Extraversion and be either high or low on Neuroticism. Similarly, a person can be low in Agreeableness and be either high or low in Conscientiousness. Thus, in the Five-Factor Model, you need five scores to describe most of an individual’s personality.

In the Appendix to this module, we present a short scale to assess the Five-Factor Model of personality (Donnellan, Oswald, Baird, & Lucas, 2006). You can take this test to see where you stand in terms of your Big Five scores. John Johnson has also created a helpful website that has personality scales that can be used and taken by the general public:

http://www.personal.psu.edu/j5j/IPIP/ipipneo120.htm

After seeing your scores, you can judge for yourself whether you think such tests are valid.

Traits are important and interesting because they describe stable patterns of behavior that persist for long periods of time (Caspi, Roberts, & Shiner, 2005). Importantly, these stable patterns can have broad-ranging consequences for many areas of our life (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). For instance, think about the factors that determine success in college. If you were asked to guess what factors predict good grades in college, you might guess something like intelligence. This guess would be correct, but we know much more about who is likely to do well. Specifically, personality researchers have also found the personality traits like Conscientiousness play an important role in college and beyond, probably because highly conscientious individuals study hard, get their work done on time, and are less distracted by nonessential activities that take time away from school work. In addition, highly conscientious people are often healthier than people low in conscientiousness because they are more likely to maintain healthy diets, to exercise, and to follow basic safety procedures like wearing seat belts or bicycle helmets. Over the long term, this consistent pattern of behaviors can add up to meaningful differences in health and longevity. Thus, personality traits are not just a useful way to describe people you know; they actually help psychologists predict how good a worker someone will be, how long he or she will live, and the types of jobs and activities the person will enjoy. Thus, there is growing interest in personality psychology among psychologists who work in applied settings, such as health psychology or organizational psychology.

Facets of Traits (Subtraits)

So how does it feel to be told that your entire personality can be summarized with scores on just five personality traits? Do you think these five scores capture the complexity of your own and others’ characteristic patterns of thoughts, feelings, and behaviors? Most people would probably say no, pointing to some exception in their behavior that goes against the general pattern that others might see. For instance, you may know people who are warm and friendly and find it easy to talk with strangers at a party yet are terrified if they have to perform in front of others or speak to large groups of people. The fact that there are different ways of being extraverted or conscientious shows that there is value in considering lower-level units of personality that are more specific than the Big Five traits. These more specific, lower-level units of personality are often called facets .

To give you a sense of what these narrow units are like, Figure 3.2.4 shows facets for each of the Big Five traits. It is important to note that although personality researchers generally agree about the value of the Big Five traits as a way to summarize one’s personality, there is no widely accepted list of facets that should be studied. The list seen here, based on work by researchers Paul Costa and Jeff McCrae, thus reflects just one possible list among many. It should, however, give you an idea of some of the facets making up each of the Five-Factor Model.

Facets can be useful because they provide more specific descriptions of what a person is like. For instance, if we take our friend who loves parties but hates public speaking, we might say that this person scores high on the “gregariousness” and “warmth” facets of extraversion, while scoring lower on facets such as “assertiveness” or “excitement-seeking.” This precise profile of facet scores not only provides a better description, it might also allow us to better predict how this friend will do in a variety of different jobs (for example, jobs that require public speaking versus jobs that involve one-on-one interactions with customers; Paunonen & Ashton, 2001). Because different facets within a broad, global trait like extraversion tend to go together (those who are gregarious are often but not always assertive), the broad trait often provides a useful summary of what a person is like. But when we really want to know a person, facet scores add to our knowledge in important ways.

Other Traits Beyond the Five-Factor Model

Despite the popularity of the Five-Factor Model, it is certainly not the only model that exists. Some suggest that there are more than five major traits, or perhaps even fewer. For example, in one of the first comprehensive models to be proposed, Hans Eysenck suggested that Extraversion and Neuroticism are most important. Eysenck believed that by combining people’s standing on these two major traits, we could account for many of the differences in personality that we see in people (Eysenck, 1981). So for instance, a neurotic introvert would be shy and nervous, while a stable introvert might avoid social situations and prefer solitary activities, but he may do so with a calm, steady attitude and little anxiety or emotion. Interestingly, Eysenck attempted to link these two major dimensions to underlying differences in people’s biology. For instance, he suggested that introverts experienced too much sensory stimulation and arousal, which made them want to seek out quiet settings and less stimulating environments. More recently, Jeffrey Gray suggested that these two broad traits are related to fundamental reward and avoidance systems in the brain—extraverts might be motivated to seek reward and thus exhibit assertive, reward-seeking behavior, whereas people high in neuroticism might be motivated to avoid punishment and thus may experience anxiety as a result of their heightened awareness of the threats in the world around them (Gray, 1981. This model has since been updated; see Gray & McNaughton, 2000). These early theories have led to a burgeoning interest in identifying the physiological underpinnings of the individual differences that we observe.

Another revision of the Big Five is the HEXACO model of traits (Ashton & Lee, 2007). This model is similar to the Big Five, but it posits slightly different versions of some of the traits, and its proponents argue that one important class of individual differences was omitted from the Five-Factor Model. The HEXACO adds Honesty-Humility as a sixth dimension of personality. People high in this trait are sincere, fair, and modest, whereas those low in the trait are manipulative, narcissistic, and self-centered. Thus, trait theorists are agreed that personality traits are important in understanding behavior, but there are still debates on the exact number and composition of the traits that are most important.

There are other important traits that are not included in comprehensive models like the Big Five. Although the five factors capture much that is important about personality, researchers have suggested other traits that capture interesting aspects of our behavior. In Figure 5 below we present just a few, out of hundreds, of the other traits that have been studied by personologists.

Not all of the above traits are currently popular with scientists, yet each of them has experienced popularity in the past. Although the Five-Factor Model has been the target of more rigorous research than some of the traits above, these additional personality characteristics give a good idea of the wide range of behaviors and attitudes that traits can cover.

The Person-Situation Debate and Alternatives to the Trait Perspective

The ideas described in this module should probably seem familiar, if not obvious to you. When asked to think about what our friends, enemies, family members, and colleagues are like, some of the first things that come to mind are their personality characteristics. We might think about how warm and helpful our first teacher was, how irresponsible and careless our brother is, or how demanding and insulting our first boss was. Each of these descriptors reflects a personality trait, and most of us generally think that the descriptions that we use for individuals accurately reflect their “characteristic pattern of thoughts, feelings, and behaviors,” or in other words, their personality.

But what if this idea were wrong? What if our belief in personality traits were an illusion and people are not consistent from one situation to the next? This was a possibility that shook the foundation of personality psychology in the late 1960s when Walter Mischel published a book called Personality and Assessment (1968). In this book, Mischel suggested that if one looks closely at people’s behavior across many different situations, the consistency is really not that impressive. In other words, children who cheat on tests at school may steadfastly follow all rules when playing games and may never tell a lie to their parents. In other words, he suggested, there may not be any general trait of honesty that links these seemingly related behaviors. Furthermore, Mischel suggested that observers may believe that broad personality traits like honesty exist, when in fact, this belief is an illusion. The debate that followed the publication of Mischel’s book was called the person-situation debate because it pitted the power of personality against the power of situational factors as determinants of the behavior that people exhibit.

Because of the findings that Mischel emphasized, many psychologists focused on an alternative to the trait perspective. Instead of studying broad, context-free descriptions, like the trait terms we’ve described so far, Mischel thought that psychologists should focus on people’s distinctive reactions to specific situations. For instance, although there may not be a broad and general trait of honesty, some children may be especially likely to cheat on a test when the risk of being caught is low and the rewards for cheating are high. Others might be motivated by the sense of risk involved in cheating and may do so even when the rewards are not very high. Thus, the behavior itself results from the child’s unique evaluation of the risks and rewards present at that moment, along with her evaluation of her abilities and values. Because of this, the same child might act very differently in different situations. Thus, Mischel thought that specific behaviors were driven by the interaction between very specific, psychologically meaningful features of the situation in which people found themselves, the person’s unique way of perceiving that situation, and his or her abilities for dealing with it. Mischel and others argued that it was these social-cognitive processes that underlie people’s reactions to specific situations that provide some consistency when situational features are the same. If so, then studying these broad traits might be more fruitful than cataloging and measuring narrow, context-free traits like Extraversion or Neuroticism.

In the years after the publication of Mischel’s (1968) book, debates raged about whether personality truly exists, and if so, how it should be studied. And, as is often the case, it turns out that a more moderate middle ground than what the situationists proposed could be reached. It is certainly true, as Mischel pointed out, that a person’s behavior in one specific situation is not a good guide to how that person will behave in a very different specific situation. Someone who is extremely talkative at one specific party may sometimes be reticent to speak up during class and may even act like a wallflower at a different party. But this does not mean that personality does not exist, nor does it mean that people’s behavior is completely determined by situational factors. Indeed, research conducted after the person-situation debate shows that on average, the effect of the “situation” is about as large as that of personality traits. However, it is also true that if psychologists assess a broad range of behaviors across many different situations, there are general tendencies that emerge. Personality traits give an indication about how people will act on average, but frequently they are not so good at predicting how a person will act in a specific situation at a certain moment in time. Thus, to best capture broad traits, one must assess aggregate behaviors, averaged over time and across many different types of situations. Most modern personality researchers agree that there is a place for broad personality traits and for the narrower units such as those studied by Walter Mischel.

The Mini-IPIP Scale

(Donnellan, Oswald, Baird, & Lucas, 2006)

Instructions: Below are phrases describing people’s behaviors. Please use the rating scale below to describe how accurately each statement describes you. Describe yourself as you generally are now, not as you wish to be in the future. Describe yourself as you honestly see yourself, in relation to other people you know of the same sex as you are, and roughly your same age. Please read each statement carefully, and put a number from 1 to 5 next to it to describe how accurately the statement describes you.

1 = Very inaccurate

2 = Moderately inaccurate

3 = Neither inaccurate nor accurate

4 = Moderately accurate

5 = Very accurate

• _______ Am the life of the party (E)
• _______ Sympathize with others’ feelings (A)
• _______ Get chores done right away (C)
• _______ Have frequent mood swings (N)
• _______ Have a vivid imagination (O)
• _______Don’t talk a lot (E)
• _______ Am not interested in other people’s problems (A)
• _______ Often forget to put things back in their proper place (C)
• _______ Am relaxed most of the time (N)
• ______ Am not interested in abstract ideas (O)
• ______ Talk to a lot of different people at parties (E)
• ______ Feel others’ emotions (A)
• ______ Like order (C)
• ______ Get upset easily (N)
• ______ Have difficulty understanding abstract ideas (O)
• ______ Keep in the background (E)
• ______ Am not really interested in others (A)
• ______ Make a mess of things (C)
• ______ Seldom feel blue (N)
• ______ Do not have a good imagination (O)

Scoring: The first thing you must do is to reverse the items that are worded in the opposite direction. In order to do this, subtract the number you put for that item from 6. So if you put a 4, for instance, it will become a 2. Cross out the score you put when you took the scale, and put the new number in representing your score subtracted from the number 6.

Items to be reversed in this way: 6, 7, 8, 9, 10, 15, 16, 17, 18, 19, 20

Next, you need to add up the scores for each of the five OCEAN scales (including the reversed numbers where relevant). Each OCEAN score will be the sum of four items. Place the sum next to each scale below.

__________ Openness: Add items 5, 10, 15, 20

__________ Conscientiousness: Add items 3, 8, 13, 18

__________ Extraversion: Add items 1, 6, 11, 16

__________ Agreeableness: Add items 2, 7, 12, 17

__________ Neuroticism: Add items 4, 9,14, 19

Compare your scores to the norms below to see where you stand on each scale. If you are low on a trait, it means you are the opposite of the trait label. For example, low on Extraversion is Introversion, low on Openness is Conventional, and low on Agreeableness is Assertive.

19–20 Extremely High, 17–18 Very High, 14–16 High,

11–13 Neither high nor low; in the middle, 8–10 Low, 6–7 Very low, 4–5 Extremely low

Outside Resources

Discussion Questions

• Consider different combinations of the Big Five, such as O (Low), C (High), E (Low), A (High), and N (Low). What would this person be like? Do you know anyone who is like this? Can you select politicians, movie stars, and other famous people and rate them on the Big Five?
• How do you think learning and inherited personality traits get combined in adult personality?
• Can you think of instances where people do not act consistently—where their personality traits are not good predictors of their behavior?
• Has your personality changed over time, and in what ways?
• Can you think of a personality trait not mentioned in this module that describes how people differ from one another?
• When do extremes in personality traits become harmful, and when are they unusual but productive of good outcomes?
• Allport, G. W., & Odbert, H. S. (1936). Trait names: A psycholexical study. Psychological Monographs, 47 , 211.
• Ashton, M. C., & Lee, K. (2007). Empirical, theoretical, and practical advantages of the HEXACO model of personality structure. Personality and Social Psychological Review, 11 , 150–166.
• Caspi, A., Roberts, B. W., & Shiner, R. L. (2005). Personality development: Stability and change. Annual Reviews of Psychology, 56 , 453–484.
• Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18 , 192–203.
• Eysenck, H. J. (1981). A model for personality .New York: Springer Verlag.
• Goldberg, L. R. (1990). An alternative description of personality: The Big Five personality traits. Journal of Personality and Social Psychology, 59 , 1216–1229.
• Gray, J. A. (1981). A critique of Eysenck’s theory of personality. In H. J. Eysenck (Ed.), A Model for Personality (pp. 246-276). New York: Springer Verlag.
• Gray, J. A. & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system (second edition) .Oxford: Oxford University Press.
• Matthews, G., Deary, I. J., & Whiteman, M. C. (2003). Personality traits . Cambridge, UK: Cambridge University Press.
• McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52 , 81–90.
• McCrae, R. R. & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60 , 175–215.
• Mischel, W. (1968). Personality and assessment . New York: John Wiley.
• Paunonen, S. V., & Ashton, M. S. (2001). Big five factors and facets and the prediction of behavior. Journal of Personality and Social Psychology, 81 , 524–539.
• Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Golberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2 , 313-345.

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11.3: Mendel’s Experiments and Heredity

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Learning Objectives

Describe Mendel’s study of garden peas and hereditary

Genetics is the study of heredity. Johann Gregor Mendel set the framework for genetics long before chromosomes or genes had been identified, at a time when meiosis was not well understood. Mendel selected a simple biological system and conducted methodical, quantitative analyses using large sample sizes. Because of Mendel’s work, the fundamental principles of heredity were revealed. We now know that genes, carried on chromosomes, are the basic functional units of heredity with the capability to be replicated, expressed, or mutated. Today, the postulates put forth by Mendel form the basis of classical, or Mendelian, genetics. Not all genes are transmitted from parents to offspring according to Mendelian genetics, but Mendel’s experiments serve as an excellent starting point for thinking about inheritance.

Mendel’s Experiments and the Laws of Probability

Johann Gregor Mendel (1822–1884) (Figure 2) was a lifelong learner, teacher, scientist, and man of faith. As a young adult, he joined the Augustinian Abbey of St. Thomas in Brno in what is now the Czech Republic. Supported by the monastery, he taught physics, botany, and natural science courses at the secondary and university levels. In 1856, he began a decade-long research pursuit involving inheritance patterns in honeybees and plants, ultimately settling on pea plants as his primary model system (a system with convenient characteristics used to study a specific biological phenomenon to be applied to other systems). In 1865, Mendel presented the results of his experiments with nearly 30,000 pea plants to the local Natural History Society. He demonstrated that traits are transmitted faithfully from parents to offspring independently of other traits and in dominant and recessive patterns. In 1866, he published his work, Experiments in Plant Hybridization, in the proceedings of the Natural History Society of Brünn.

Mendel’s work went virtually unnoticed by the scientific community that believed, incorrectly, that the process of inheritance involved a blending of parental traits that produced an intermediate physical appearance in offspring; this hypothetical process appeared to be correct because of what we know now as continuous variation. Continuous variation results from the action of many genes to determine a characteristic like human height. Offspring appear to be a “blend” of their parents’ traits when we look at characteristics that exhibit continuous variation. The blending theory of inheritance asserted that the original parental traits were lost or absorbed by the blending in the offspring, but we now know that this is not the case. Mendel was the first researcher to see it. Instead of continuous characteristics, Mendel worked with traits that were inherited in distinct classes (specifically, violet versus white flowers); this is referred to as discontinuous variation . Mendel’s choice of these kinds of traits allowed him to see experimentally that the traits were not blended in the offspring, nor were they absorbed, but rather that they kept their distinctness and could be passed on. In 1868, Mendel became abbot of the monastery and exchanged his scientific pursuits for his pastoral duties. He was not recognized for his extraordinary scientific contributions during his lifetime. In fact, it was not until 1900 that his work was rediscovered, reproduced, and revitalized by scientists on the brink of discovering the chromosomal basis of heredity.

Mendel’s Model System

Mendel’s seminal work was accomplished using the garden pea, Pisum sativum , to study inheritance. This species naturally self-fertilizes, such that pollen encounters ova within individual flowers. The flower petals remain sealed tightly until after pollination, preventing pollination from other plants. The result is highly inbred, or “true-breeding,” pea plants. These are plants that always produce offspring that look like the parent. By experimenting with true-breeding pea plants, Mendel avoided the appearance of unexpected traits in offspring that might occur if the plants were not true breeding. The garden pea also grows to maturity within one season, meaning that several generations could be evaluated over a relatively short time. Finally, large quantities of garden peas could be cultivated simultaneously, allowing Mendel to conclude that his results did not come about simply by chance.

Mendelian Crosses

Mendel performed hybridizations , which involve mating two true-breeding individuals that have different traits. In the pea, which is naturally self-pollinating, this is done by manually transferring pollen from the anther of a mature pea plant of one variety to the stigma of a separate mature pea plant of the second variety. In plants, pollen carries the male gametes (sperm) to the stigma, a sticky organ that traps pollen and allows the sperm to move down the pistil to the female gametes (ova) below. To prevent the pea plant that was receiving pollen from self-fertilizing and confounding his results, Mendel painstakingly removed all of the anthers from the plant’s flowers before they had a chance to mature.

Plants used in first-generation crosses were called P 0 , or parental generation one, plants (Figure 3). Mendel collected the seeds belonging to the P 0 plants that resulted from each cross and grew them the following season. These offspring were called the F 1 , or the first filial ( filial = offspring, daughter or son), generation. Once Mendel examined the characteristics in the F 1 generation of plants, he allowed them to self-fertilize naturally. He then collected and grew the seeds from the F 1 plants to produce the F 2 , or second filial, generation. Mendel’s experiments extended beyond the F 2 generation to the F 3 and F 4 generations, and so on, but it was the ratio of characteristics in the P 0 −F 1 −F 2 generations that were the most intriguing and became the basis for Mendel’s postulates.

Garden Pea Characteristics Revealed the Basics of Heredity

In his 1865 publication, Mendel reported the results of his crosses involving seven different characteristics, each with two contrasting traits. A trait is defined as a variation in the physical appearance of a heritable characteristic. The characteristics included plant height, seed texture, seed color, flower color, pea pod size, pea pod color, and flower position. For the characteristic of flower color, for example, the two contrasting traits were white versus violet. To fully examine each characteristic, Mendel generated large numbers of F 1 and F 2 plants, reporting results from 19,959 F 2 plants alone. His findings were consistent.

What results did Mendel find in his crosses for flower color? First, Mendel confirmed that he had plants that bred true for white or violet flower color. Regardless of how many generations Mendel examined, all self-crossed offspring of parents with white flowers had white flowers, and all self-crossed offspring of parents with violet flowers had violet flowers. In addition, Mendel confirmed that, other than flower color, the pea plants were physically identical.

Once these validations were complete, Mendel applied the pollen from a plant with violet flowers to the stigma of a plant with white flowers. After gathering and sowing the seeds that resulted from this cross, Mendel found that 100 percent of the F 1 hybrid generation had violet flowers. Conventional wisdom at that time would have predicted the hybrid flowers to be pale violet or for hybrid plants to have equal numbers of white and violet flowers. In other words, the contrasting parental traits were expected to blend in the offspring. Instead, Mendel’s results demonstrated that the white flower trait in the F 1 generation had completely disappeared.

Importantly, Mendel did not stop his experimentation there. He allowed the F 1 plants to self-fertilize and found that, of F 2 -generation plants, 705 had violet flowers and 224 had white flowers. This was a ratio of 3.15 violet flowers per one white flower, or approximately 3:1 . When Mendel transferred pollen from a plant with violet flowers to the stigma of a plant with white flowers and vice versa, he obtained about the same ratio regardless of which parent, male or female, contributed which trait. This is called a reciprocal cross —a paired cross in which the respective traits of the male and female in one cross become the respective traits of the female and male in the other cross. For the other six characteristics Mendel examined, the F 1 and F 2 generations behaved in the same way as they had for flower color. One of the two traits would disappear completely from the F 1 generation only to reappear in the F 2 generation at a ratio of approximately 3:1 (Table 1).

• May 31, 2022
• 10 min read

Personality 101: The Trait Approach & the Lexical Hypothesis

Human personality is a well-known concept in both academic and non-academic circles. This concept has raised the most diverse conclusions in both circles: from well-established factorial solutions to classifications of people based on what the Sorting Hat from Hogwarts would estimate. For a long time now, research in psychology has gained plenty of knowledge about human personality and its implications in everyday life, but this knowledge is either unknown or misunderstood by the general population. This situation calls for efforts to close this gap between what is known based on science and what is assumed to be true based on our random and subjective experience. The following 101 series explores the most consensual contemporary conceptualization of personality, the creative steps scientists took to arrive at this conceptualization using the lexical hypothesis, the specificities of this contemporary conceptualization by looking at each one of the Big Five traits, and the implications of each trait for individuals and societies.

The Personality 101 series is divided into 5 chapters:

Personality 101: The Trait Approach and The Lexical Hypothesis

Personality 101: Conscientiousness

Personality 101: Agreeableness and Extraversion

Personality 101: Emotional Stability

Personality 101: Openness to Experience

Thirty-one -year-old Robert often feels restless. He has problems sitting at a desk for more than a few minutes, cannot get organized, loses his keys and wallet, and forgets about his plans for the evening. He fails to achieve up to his potential at work. During the conversation, his mind wanders and he interrupts others, blurting out what he is thinking without considering the consequences. He gets into arguments. His mood swings and periodic outbursts make life difficult for those around him. Now his marriage is in trouble (John, 2021) .

This is a typical description of someone’s personality in a clinical setting. It conveys a good idea of how Robert is: disorganized with things and time causing evident consequences in his work, disorganized in speech, restless and impulsive. With this small paragraph, a clinician might already have an idea of what Robert is and what the goals would be in clinical intervention. However, something is lacking here. Surely, a short paragraph cannot summarize the wholeness of a human being, all their intricacies and idiosyncrasies; besides, the paragraph lacks some mention of the positive characteristics, too. Despite the risk of losing information, the task of trying to summarize someone's personality is useful, it helps with making life decisions easier: decisions like the search for a job, choosing a career, a partner, an adequate therapeutic procedure, and even one’s friends and hobbies.

What is needed is to summarize the personality of someone with the least loss of information possible, i.e. a descriptive model or a taxonomy of personality. “One of the central goals of scientific taxonomies is the definition of overarching domains within which large numbers of specific instances can be understood in a simplified way” (John, 2021, p. 38 ) . In plain words, a taxonomy about personality would be useful because it will allow the interpretation of the massive amount of information contained in one person’s behaviour using a very small group of categories. In this article, the best taxonomy for personality is going to be introduced along with the methodology followed to design it.

A taxonomy of human personality has been searched for a long time. Even in Ancient Greece Theophrastus would ponder: “why is that, while all Greece lies under the same sky and all the Greeks are educated alike, it has befallen us to have characters variously constituted?” (Theophrastus, 1909, p.77) . The most famous of the ancient attempts is Hippocrates' taxonomy in which he believed that different proportions of four bodily fluids or humour would manifest in the way people think, feel, and behave. A predominance of blood constituted a sanguine or social character, phlegm constituted a phlegmatic or easygoing character, black bile constituted a melancholic or analytical character, and yellow bile constituted a choleric or extraverted character (Chiao, 2018) .

Before going into more taxonomies of traits a note of recognition must be conceded to many other conceptualizations of personality that are not trait-based. Whereas the trait approach is one of the most frequently used nowadays, many authors in the history of psychology proposed models based on their own scientific and theoretical framework (Funder, 2012) . Freud’s framework, for instance, is based on the psychosexual development of the person, and he would argue that personality suffers many changes during childhood, also known as the stages of psychosexual development, but once adolescence arrives, personality becomes rather stable. Another key aspect of his theory is the division of personality into three components: the less conscious aspect, the id; the conscious experience of the person, the ego; and the social demands internalized in the individual, the super-ego (Funder, 2012) .

Later, other authors would propose different psychological processes as important components of human experience. Jung proposed the collective unconscious, the Anima and the Shadow as crucial mechanisms of the human psyche. More humanistic perspectives, like the ones championed by Abraham Maslow, Carl Rogers or Positive Psychology, focused on self-actualization processes, the acceptance of one's own experience, the hierarchies of motivations and needs, and the positive aspects of psychology like strengths and virtues (Funder, 2012) .

These are important contributions but some of them are elusive to scientific investigation, meaning that it is hard to apply the scientific method to answer the questions they pose. This is why, currently, they do not receive the same attention as the trait approach, and though there are some attempts to understand them, their significance is not as universal as the trait approach (Mastnak, 2021) .

The most basic tenet of the trait approach is, understandably, the trait. According to Allport (1931) , a trait has more than a nominal existence, is more than a generalized habit, is dynamic, it can be empirically or statistically found, is not unrelated to other traits, is not a moral quality, is not disproven if other behaviors appear in the behavioral repertoire of an individual, and it can be spotted in the individual and also in the population. Therefore, a trait is something real, can be found using statistical tools, and it is not disproven if other behaviors or emotions that go against this trait appear. This last feature of traits reveals an important nuance: a trait is the natural tendency of an individual, what the person would do almost spontaneously, the default mode of operating; this does not mean that, in a particular situation, an individual could not show behaviors, feelings, or thoughts that deviate from this natural tendency (Fleeson & Law, 2015) . Practically, this means that an extravert can sometimes act as an introvert, and vice versa.

Once the concept of trait has been cleared, let’s look at some of the attempts to define a taxonomy of personality in the modern history of Psychology. Raymond Cattell, a British-American psychologist, proposed 16 factors, or overarching categories obtained by means of statistical procedures, that comprise traits like Warmth or being outgoing and supportive, Social Assertiveness or being uninhibited and bold in social situations, Introversion or being reserved and clear-headed, and Independence or being self-sufficient (Cattell & Mead, 2008) . His theory led to the development of the Sixteen Personality Factor Questionnaire (16PF) that is still being used in vocational and educational settings. Another well-known theory of traits is the Eysencks’ theory of personality, in which there were only two bipolar factors accounting for all the variation in human personality: extroversion-introversion, and emotional stability-instability (Furnham et al., 2008) .

The methodology used by these authors to obtain such taxonomies is mostly based on the following process. The author would first get enough information about the topic by reading or gathering the conclusions obtained after years of experience in therapy and consultation. Once they think they have a solid theoretical framework from which to talk about personality, they will enumerate a set of traits that could explain and summarize humans across time and places. Although this is an oversimplification of the process, it serves one purpose: to show that, although it may be helpful for the patients of the author, it is not replicable or its replicability could be easily questioned. Therefore, since human personality is a universal phenomenon, a taxonomy that could replicate itself across contexts and individuals is needed (Mischel, 1996) .

There is a different process that overcomes the limitations of the previous one. This is the so-called lexical hypothesis, proposed by Galton (1949) , which states that every important human phenomenon must be somehow represented in the lexicon of a language, and since most languages are easily translated to others, the universality of the phenomenon could also be guaranteed. Based on this proposition, what authors would normally do is gather all the words used in a particular language for the phenomenon of interest from a representative sample of words in that language (some examples include dictionaries but also the transcripts of contemporary famous TV shows and movies), then they would ask a group of experts to analyze this list and determine which words are better at capturing the phenomenon under study (Ashton & Lee, 2007; Oreg et al., 2020; Parrigon et al., 2017) . This implies discarding synonyms and uncommon words from the sample. Later, they would approach a representative sample of individuals to categorize the phenomenon of interest (e.g. personality) according to the words established in the previous step. This will allow, by using proper psychometric and statistical techniques (i.e., factorial and multivariate statistical analyses), both to filter the best words that will be used and to create a refined measure of the phenomenon.

As of now, the scientific consensus is that the lexical hypothesis is the best solution found so far for the classification and understanding of personality (DeYoung et al., 2007) . In fact, one of the most famous and used taxonomies used at the moment, both in research, educational, clinical, and vocational settings, is the Big Five Taxonomy of personality, initially proposed by Costa & McCrae (1992) , and that have been further developed ever since. This taxonomy summarizes personality in five overarching traits: Conscientiousness, Agreeableness, Emotional Stability, Openness to Experience, and Extraversion. Each one of this will be further developed in the following posts of this 101 series.

One important note of warning is that, although this Big Five solution has proven to be very useful, the traits that conform it should not be understood as casual entities of human behaviors, instead they should be understood according to their real nature: a descriptive explanation of human personality. They do not explain casual relationships, they describe personality, they summarize it so that it can be easier to understand it (Fajkowska & Kreitler, 2018) . A second note is that these traits are not separable and completely distinguishable entities in and of themselves, they are correlated and there is some degree of overlap between some of them (Van der Linden et al., 2012) .

A final note, and the most important one, is that this taxonomy is based, as almost everything else in Psychology, on self-reports accounts. This is an important issue because, as Jung would say, “you are not what you say you’ll do, but what you do” (PsycholoGenie, 2014) . This fact poses a challenge, specially in personality research: are the tests really measuring psychological phenomena if they rely solely on self-report accounts? Are scientists not purposefully biasing their findings because self-reports suppose cheaper costs in research than observational or experimental reports (Galic et al., 2016; Olino & Klein, 2015) ? These questions are fueling some alternative research directions, like gathering behavioral data by means of wearables, cameras, and smartphones which, as of now, are both showing coincidences with the already extant research on the topic, and pushing its boundaries forward (Ihsan & Furnham, 2018) .

Personality psychology is an important and flourishing branch of Psychology. Its aim is to better understand human behaviors, thoughts, and emotions so that its knowledge would allow people to make better informed decisions in their life. Throughout history, personality has raised many questions to philosophers, writers, thinkers, and scientists and there have been many attempts to understand it. Though the validity of some of them could be recognized from a phenomenological point of view, the scientific method is not yet capable of working with them. Now, the consensus obtained in science is that the best solution found so far is the lexical hypothesis, as manifested in the relevance and extended use of the Big Five Theory of personality. These five traits have been proven useful in the description of many aspects of the human experience and their research is still a burgeoning theme in Psychology, though they are not free of limitations and improvements. Contemporary technologies are being gradually incorporated in the study of personality and their conclusions are solidifying the field and incorporating new findings. Only time will show how far the study of personality will get, and it is almost breathtaking to think that it all started with a man pondering why there were so many differences in people that were born under the same Ancient Greek sky.

Bibliographical References

Allport, G. W. (1931). What is a trait of personality? The Journal of Abnormal and Social Psychology , 25 (4), 368–372. https://doi.org/10.1037/h0075406

Ashton, M. C., & Lee, K. (2007). Empirical, Theoretical, and Practical Advantages of the HEXACO Model of Personality Structure. Personality and Social Psychology Review , 11 (2), 150–166. https://doi.org/10.1177/1088868306294907

Cattell, H. E. P., & Mead, A. D. (2008). The Sixteen Personality Factor Questionnaire (16PF). In The SAGE handbook of personality theory and assessment, Vol 2: Personality measurement and testing (pp. 135–159). Sage Publications, Inc. https://doi.org/10.4135/9781849200479.n7

Chiao, E. (2018, October 4). New study reveals four major personality types . The Johns Hopkins News-Letter. https://www.jhunewsletter.com/article/2018/10/new-study-reveals-four-major-personality-types

Costa, P. T., & McCrae, R. R. (1992). Four ways five factors are basic. Personality and Individual Differences , 13 (6), 653–665. https://doi.org/10.1016/0191-8869(92)90236-I

DeYoung, C. G., Quilty, L. C., Peterson, J. B., & nueva, E. a sitio externo E. enlace se abrirá en una ventana. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology , 93 (5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880

Fajkowska, M., & Kreitler, S. (2018). Status of the Trait Concept in Contemporary Personality Psychology: Are the Old Questions Still the Burning Questions? Journal of Personality , 86 (1), 5–11. https://doi.org/10.1111/jopy.12335

Fleeson, W., & Law, M. K. (2015). Trait enactments as density distributions: The role of actors, situations, and observers in explaining stability and variability. Journal of Personality and Social Psychology , 109 (6), 1090–1104. https://doi.org/10.1037/a0039517

Funder, D. C. (2012). The Personality Puzzle (pp. xxvii, 466). WW Norton & Co.

Furnham, A., Eysenck, S. B. G., & Saklofske, D. H. (2008). The Eysenck personality measures: Fifty years of scale development. In The SAGE handbook of personality theory and assessment, Vol 2: Personality measurement and testing (pp. 199–218). Sage Publications, Inc. https://doi.org/10.4135/9781849200479.n10

Galic, Z., Bubić, A., & Parmac Kovacic, M. (2016). Alternatives to self-reports: Conditional reasoning problems and IAT-based tasks. In The Wiley Handbook of Personality Assessment (p. 215.-227.). https://doi.org/10.1002/9781119173489.ch16

Galton, F. (1949). The Measurement of Character. In Readings in general psychology (pp. 435–444). Prentice-Hall, Inc. https://doi.org/10.1037/11352-058

Ihsan, Z., & Furnham, A. (2018). The new technologies in personality assessment: A review. Consulting Psychology Journal: Practice and Research , 70 (2), 147–166. https://doi.org/10.1037/cpb0000106

John, O. P. (2021). History, measurement, and conceptual elaboration of the Big‑Five trait taxonomy: The paradigm matures. In Handbook of personality: Theory and research, 4th ed (pp. 35–82). The Guilford Press.

Mastnak, W. (2021). Psychoanalysis and Qualitative Factor Analysis: A comparative meta- theoretical perspective . https://doi.org/10.13140/RG.2.2.11022.89922

Mischel, W. (1996). Personality and Assessment . Psychology Press. https://doi.org/10.4324/9780203763643

Olino, T. M., & Klein, D. N. (2015). Psychometric Comparison of Self- and Informant-Reports of Personality. Assessment , 22 (6), 655–664. https://doi.org/10.1177/1073191114567942

Oreg, S., Edwards, J. A., & Rauthmann, J. F. (2020). The situation six: Uncovering six basic dimensions of psychological situations from the Hebrew language. Journal of Personality and Social Psychology , 118 (4), 835–863. https://doi.org/10.1037/pspp0000280

Parrigon, S., Woo, S. E., Tay, L., & Wang, T. (2017). CAPTION-ing the situation: A lexically-derived taxonomy of psychological situation characteristics. Journal of Personality and Social Psychology , 112 (4), 642–681. https://doi.org/10.1037/pspp0000111

PsycholoGenie. (2014, August 12). A Comprehensive Collection of 60 Famous Quotes By Carl Jung. Psychologenie . https://psychologenie.com/collection-of-famous-quotes-by-carl-jung

Theophrastus. (1909). The characters of Theophrastus (R. C. Jebb, Trans. & J. E. Sandys, Ed.). London: Macmillan.

Van der Linden, D., Tsaousis, I., & Petrides, K. V. (2012). Overlap between General Factors of Personality in the Big Five, Giant Three, and trait emotional intelligence. Personality and Individual Differences , 53 (3), 175–179. https://doi.org/10.1016/j.paid.2012.03.001

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• Personality

What Character Traits Do You Have?

What makes up your personality, and which personality traits you have.

Posted November 15, 2021 | Reviewed by Michelle Quirk

• What Is Personality?
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• Personality traits can be thought of as habitual individual differences in behavior, thought, and emotion.
• The "Big Five" personality traits are extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience.
• The social-cognitive theory of personality argues that personality is dynamic and changes as a result of our circumstances.

What is personality ? Well, the answer depends on whom you ask. Personality may be part of our unconscious . It may consist of personal narratives that we build across our lives (Cervone, Shadel, & Jencius, 2001). Or, it may be the observable manifestation of our genetics . But overall, personality traits can be thought of as habitual individual differences in behavior, thought, and emotion .

Theory of Personality

It wasn’t until recently that we even knew we had personalities. Still, for a long time, we’ve used adjectives to describe people. For example, we might say someone is responsible, innovative, angry, or friendly. Each of these adjectives can also be thought of as personality traits.

​Interestingly, when researchers analyzed these common adjectives, they found that they clumped into five categories (Goldberg, 1993). These categories are now known as the Big Five personality traits . And each of these Big Five aspects of personality includes hundreds if not thousands of personality traits (Goldberg, 1993).

The Big Five are:

• Extraversion (vs. introversion ): This includes activity, assertiveness , attention seeking, gregariousness, sociability, and vigor.
• Agreeableness (vs. hostility): This includes cooperation , empathy , friendliness, sensitivity, nurturance, tolerance, warmth, and understanding.
• Conscientiousness (vs. undependability): This includes achievement, autonomy, order, control, self-sufficiency, and constraint.
• Neuroticism (vs. emotional stability ): This includes anger , depression , anxiety , hostility, guilt proneness, and emotional intensity.
• Openness to experience (vs. close-mindedness): This includes creativity , novelty seeking, thoughtfulness, and imagination (DeNeve & Cooper, 1998).

Although there are only five primary personality traits, we can fall anywhere on the continuum of these traits. In other words, we are not 100 percent extrovert or 100 percent introvert. Rather, we might be mostly extroverted, mostly introverted, or somewhere in the middle.

To see where you fall on these Big Five traits, here is a short personality quiz with some of the questions used in research on the Big Five personality traits (Saucier, 1997; https://ipip.ori.org ).

Extraversion

I am open about my feelings.

Strongly disagree Strongly agree

1 2 3 4 5 6 7 8 9 10

I take charge.

I talk to a lot of different people at parties.

I make friends easily.

I'm never at a loss for words.

1 2 3 4 5 6 7 8 9 10 ​

Conscientiousness

I do things by the book.

I try to follow the rules.

I believe laws should be strictly enforced.

I pay attention to details.

I like order.

Emotional Stability

I seldom feel blue.

I am relaxed most of the time.

I feel comfortable with myself.

I am not easily bothered by things.

I take things as they come.

Agreeableness

I feel others' emotions .

I have a soft heart.

I sympathize with others' feelings.

I am concerned about others.

I make people feel at ease.

I enjoy the beauty of nature.

I believe in the importance of art.

I love to reflect on things.

I see beauty in things that others might not notice.

I need a creative outlet.

Add up your score for each of the five personality factors. The higher your score, the stronger each of these personality traits is for you.

​Other Theories of Personality

Although the Big Five theory of personality is the most popular, you may also be interested in the social-cognitive theory of personality. This theory states that much of our behavior — what we might consider to be personality — arises as a direct result of social stimuli. While the Big Five theory of personality assumes that personality consists of our essential, unchangeable, innate qualities, the social-cognitive theory of personality argues that personality itself is dynamic and changes as a result of our circumstances (Cervone, Shadel, & Jencius, 2001).

Regardless of where personality comes from, it can be helpful for our understanding of ourselves to know where we fall and what traits we have.

Adapted from an article published by The Berkeley Well-Being Institute .

Cervone, D., Shadel, W. G., & Jencius, S. (2001). Social-cognitive theory of personality assessment. Personality and Social Psychology Review , 5(1), 33–51.

DeNeve, K. M., & Cooper, H. (1998). The happy personality: a meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin , 124(2), 197.

Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist, 48(1), 26.

Saucier, G. (1997). Effects of variable selection on the factor structure of person descriptors. Journal of Personality and Social Psychology , 73(6), 1296.

Tchiki Davis, Ph.D. , is a consultant, writer, and expert on well-being technology.

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Lexical Hypothesis

The Lexical Hypothesis is a significant concept in the field of personality psychology. Broadly speaking, it proposes that the most relevant and universally acknowledged human personality traits are encoded in our language. These traits are believed to be so crucial to communication and social interaction that our ancestors developed specific terms to refer to them, allowing for efficient description and discussion of individual differences.

Understanding the Lexical Hypothesis

The historical perspective of the lexical hypothesis.

The Lexical Hypothesis is deeply rooted in the annals of psychology and language. Tracing back to Sir Francis Galton in the 19th century, this hypothesis has been a pivotal principle in understanding personality traits. Galton, a pioneer in psychometrics and behavioral genetics, proposed that the complexities of human personality could be deciphered by exploring the richness of language. His core belief was that those personality traits deemed significant for survival and societal functioning would become an integral part of our language.

The Mechanism of the Lexical Hypothesis

The Lexical Hypothesis operates on two primary assumptions. The first, known as the synonym frequency principle, suggests that the more significant a personality trait is, the more synonyms our language has for it. This idea manifests when we realize how many ways we can express a simple trait like ‘happy’ – joyful , cheerful, elated, content, and so forth.

The second principle, termed the cross-cultural universality principle, posits that if a trait is crucially important, it will surface in the language of every culture. This principle suggests a universality in essential human characteristics across different cultures and societies.

The Lexical Hypothesis and Modern Personality Assessment

The Lexical Hypothesis provides the cornerstone for many modern personality assessment techniques. For instance, the widely used Big Five model, also known as the Five-Factor Model (FFM), is heavily rooted in the lexical approach. By examining language, psychologists could extract broad dimensions of personality, offering a comprehensive framework to assess and predict human behavior in a variety of contexts, from professional settings to personal relationships.

Examples of the Lexical Hypothesis in Action

Big five personality traits.

One of the best examples of the Lexical Hypothesis in practice is the Big Five personality traits model. The traits included in this model – openness, conscientiousness, extraversion, agreeableness, and neuroticism – were identified through a process known as factor analysis. Researchers looked at a large number of personality-describing adjectives in the dictionary and identified clusters of words that seemed to describe similar qualities. These clusters then became the basis for the Big Five traits.

Personality Adjectives in Daily Life

Every day, we use adjectives like “kind,” “brave,” “lazy,” and “funny” to describe people’s personalities . These words, part of our everyday vocabulary, are practical manifestations of the Lexical Hypothesis. They reflect those traits that our society deems important enough to name and discuss.

The Future of the Lexical Hypothesis

Implications for personality research.

The Lexical Hypothesis continues to hold a significant place in personality psychology, guiding researchers as they explore and map the complexities of human personality. It also offers exciting potential for cross-cultural studies, as examining the personality-describing words in different languages could provide valuable insights into culturally specific understandings of personality.

Limitations and Critiques

Despite its widespread acceptance, the Lexical Hypothesis is not without its critics. Some argue that the importance of language in encapsulating personality traits could be overstated, and there may be culturally specific or non-verbal aspects of personality that are overlooked. Nevertheless, the hypothesis provides a valuable framework for exploring the fascinating landscape of human personality.

Understanding the Lexical Hypothesis provides a compelling glimpse into the ways our language and personalities are intertwined. As we continue to explore this relationship, we come closer to unraveling the intricacies of the human personality and our shared humanity.

• Open access
• Published: 09 May 2024

Patterns and driving factors of functional traits of desert species with different elevational distributions in the Tibetan Plateau and adjacent areas

• Ya Hu 1 , 2 ,
• Xiangyun Li 1 , 2 ,
• Shaokun Wang 1 , 2 ,
• Peng Lv 1 , 2 ,
• Ping Yue 1 , 2 ,
• Min Chen 1 , 2 &
• Xiaoan Zuo 1 , 2  

BMC Plant Biology volume  24 , Article number:  371 ( 2024 ) Cite this article

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Variations in functional traits serve as measures of plants’ ability to adapt to environment. Exploring the patterns of functional traits of desert plants along elevational gradients is helpful to understand the responses and adaptation strategies of species to changing environments. However, it is unknown whether the relationship between functional traits and elevation is affected by differences in the species’ elevational distributions (elevation preference and species’ range). Importantly, most researches have concerned with differences in mean trait values and ignored intraspecific trait variation. Here, we measured functional traits of desert plants along a wide elevational gradient in the Tibetan Plateau and adjacent areas and explored functional trait patterns over elevation in species with different elevational distributions. We decomposed trait variation and further investigated characterizations of intraspecific variation. Ultimately, the main drivers of trait variation were identified using redundancy analysis. We found that species’ elevational distributions significantly influenced the relationship of functional traits such as plant height, leaf dry matter content, leaf thickness, leaf nitrogen and carbon content with elevation. Species with a lower elevational preference showed greater trait variation than species with a higher elevational preference, suggesting that species that prefer high elevation are more conservative facing environmental changes. We provide evidence that interspecific trait variation in leaf thickness and leaf carbon content decreased with increasing species’ range, indicating that increased variations in resistance traits within species make greater responsiveness to environmental changes, enabling species a wider range. Elevation, temperature and precipitation were the main drivers of trait variation in species with a low elevational preference, while the effect of precipitation on trait variation in species with a high elevational preference was not significant. This study sheds new insights on how plants with different elevational distributions regulate their ecological strategies to cope with changing environments.

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Introduction

Predicting how different specie will respond to environmental changes is challenging due to the diversity of natural ecosystems [ 1 ]. Functional traits provide a method for disentangling community responses to environmental changes by linking environment with individual performance [ 2 , 3 , 4 ]. Functional traits are measurable characteristics of an individual that represent species adaptive responses to abiotic and biotic factors [ 5 , 6 ]. There has been a classic problem in ecology on what cause functional trait variations along climate gradients [ 4 , 7 ]. This knowledge is crucial for foreseeing how climate changes will affect species, species interactions, and ecosystem functioning [ 8 ]. The distribution of functional traits along environmental gradients is explained by mechanisms relating to physiological constraints on species [ 9 ]. Physiology-based theories presuppose that changes in the physical environment and physiological restraints control the distribution and evolution of organisms, with implications for the distribution of morphological traits [ 8 ].

Elevational gradients offer suitable environments for enhancing inference of the mechanical causes of ecosystem functioning due to their varied environmental and climatic circumstances [ 6 ]. Some functional traits, particularly those that relate to plant height, leaf size and resource acquisition, are strong predictors of plant performance, differ between species, and can be used to infer changes in ecosystem functioning at broad ecological scales [ 10 , 11 ]. For example, with increasing elevation, leaves get thicker and smaller [ 4 ]. In harsher situations, plant height and leaf size tend to decline, whereas leaf nutrient contents vary with leaf morphology, elevation, and climatic conditions [ 12 ]. The variations in functional trait over elevational gradients are expected to explain plant ecological strategies [ 13 ].

Trait-based ecology has, up to this point, mostly emphasized the differences in traits between species [ 2 , 14 , 15 ]. However, there is mounting evidence that intraspecific variation, rather than interspecific variations, contributes more to trait variation caused by environmental factors [ 1 , 16 , 17 ]. Intraspecific variation accounts for about a quarter of total trait variation globally [ 17 ], but this proportion is predicted to increase in harsh environments due to the filtering effect of environment on the trait expression [ 18 ]. Large intraspecific trait variation may conceal or change the relationships among interspecific traits, limiting the usefulness of interspecific variation for ecological prediction at different scales [ 19 ]. Therefore, investigating intraspecific trait variations may provide a more comprehensive answer to community construction and ecosystem function maintenance [ 17 ].

The Tibetan Plateau region is characterized by high average elevations and wide elevational gradients, due to the topographic features and the atmospheric circulation characteristics, harboring not only unique alpine ecosystems, but also a variety of natural ecosystem types such as forests, meadows, steppes and deserts appear from southeast to northwest [ 20 , 21 ]. Therefore, the Tibetan Plateau has nurtured many unique plant resources with high biological and genetic research values, which are important for biodiversity maintenance and biological resource conservation [ 21 , 22 ]. The Tibetan Plateau is not only an important ecological security barrier, but also a sensitive and fragile zone to global climate changes. As the global climate change process advances, glacial retreat, permafrost melting and desertification are becoming more prominent, accelerating the degradation of vegetation in the Tibetan Plateau [ 23 ]. Exploring the status of desert plants on the Tibetan Plateau can provide a theoretical basis for desertification control, biological resource conservation and sustainable development of the ecosystem.

Plants respond differently to different elevational gradients, but there are fewer studies on the response of traits to different elevational distribution ranges. Interspecific and intraspecific trait variations are major components of plant functional trait variation, but intraspecific trait variations across a large elevational gradient merits further research [ 2 , 24 ]. The objectives of the present study were: (i) to establish the relationship between functional traits and elevation in desert species with different elevational preferences and elevational distribution ranges; (ii) to explore the sources of variation of functional traits and to determine the proportion of intraspecific variation and (iii) to identify the main environmental factors that influence functional traits of species with different elevational distributions.

Materials and methods

According to comprehensive natural geographic zoning, distribution of deserts and desertification in China and land use conditions, we selected a typical desert ecosystem of the Tibetan Plateau and adjacent areas as our study area. The region reaches an average elevation of 4000 m a.s.l., and nearly a quarter of its northwestern area is alpine, with altitudes above 5000 m a.s.l. The moisture status of the study area has large differences, with annual precipitation mostly below 900 mm, decreasing from east to west and from south to north. The sampling sites were selected in the desert ecosystems of the Tibetan Plateau and adjacent areas, ranging from 813 to 5930 m a.s.l.

Sampling and trait measurements

A total of 414 study sites were selected in the desert ecosystems of the Tibetan Plateau and adjacent areas, and vegetation surveys were conducted over 4 years (Table S1 ). At each study site, typical and representative plant communities were selected and a 100 m × 100 m sampling area was established. Within the sampling area, five 10 m×10 m shrub sampling plots and nine 1 m×1 m grass sampling plots were set up to investigate the species composition in shrub and grass sampling plots, respectively.

For dominant species of the community, 6 functional traits were measured based on the relevance to plant survival strategies and the feasibility of field measurements [ 10 , 25 ], including plant height, specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness (LT), leaf carbon content (LCC) and leaf nitrogen content (LNC). Plant height of each species was measured at the same time as the vegetation survey distancing from soil to highest leaf. We selected 5–10 individuals and at least 10 leaves from each dominant species within a site for determination of functional traits. The SLA, LDMC and LT were measured referring to standard protocols [ 25 ]. The leaves were dried and crushed for the determination of LCC and LNC by elemental analyzer (Costech, Milano, Italy).

Elevational distribution indicators

The elevational preference (EP) and species’ range (SR) can be used to explain two aspects of elevational distributions, reflecting the species’ preference for habitat elevation and the range over which the species can be distributed, respectively [ 1 ]. A species’ EP represents its median elevation in relation to all species, calculating by the following formula. EP ranges from 0 to 1, with values close to 0 for species with median elevation that is near to the lower elevation and 1 for species with median elevation that is near to the higher elevation.

Where ‘Elevational preference i ’ is the elevational preference of species i, ‘Ele (Med) i ’ is the median elevation of species i, ‘Ele (Max)’ is the maximum elevation of all species and ‘Ele (Min)’ is the minimum elevation of all species.

We calculated each target species’ SR, which reflects its elevational distribution in comparison to all species. SR ranges between 0 and 1 with values near to 0 for species with narrower elevation ranges, and 1 for species with wider elevational ranges. We estimated the SR by the following equation.

Where ‘Species range i ’ is the distribution range of species i, ‘Ele (Max) i ’ is the maximum elevation of species i and ‘Ele (Min) i ’ is the minimum elevation of species i.

Statistical analysis

To examine the patterns of plant trait variation in desert species with different elevational distributions over elevation, we constructed mixed-effects models for plant height, SLA, LDMC, LT, LNC and LCC using the lmer function from the lme4 package.

By fitting linear mixed effects models with a fixed intercept and random effects for region, site, functional group and species, we quantified the amount of trait variation for each species and trait at each nested scale using variance decomposition. The random effect variances in this equation stand in for variance between regions, sites, functional groups and species, whereas the residual variance represents samples within species (intraspecific trait variation).

We ran a redundancy analysis (RDA) on all trait measurements and elevation, temperature, precipitation, soil pH, soil electrical conductivity (EC), soil clay content (Clay), soil sand content (Sand), soil nitrogen content (SNC) and soil carbon content (SCC) to determine the relationships between functional traits and environmental factors. RDA was analyzed by the rda function form the vegan package. All the data analysis was carried out using R (R Development Core Team 2022).

Patterns of desert plant traits along elevational gradients

At the overall level, plant height, LT, LNC and LCC gradually decreased with increasing elevation (Figure S2 ). In detail, height showed a decreasing overall pattern with elevation in the responses of most species, meaning higher elevations resulted in shorter plants. Although trends varied widely among species, more than half of the species showed a decreasing trend, resulting in a significant decline in LT and LCC with elevation. However, there were few species with a decreasing trend, but overall LNC decreased significantly along elevation, probably due to interspecific differences. Moreover, Differences between species trends may account for the non-significant relationship between SLA, LDMC and elevation (Figure S3 - S8 ).

Relationships between functional traits and elevation for species with different elevational distributions

Elevation distributions, namely EP and SR, largely influences the relationship between functional traits and elevation. There were significant interactions between elevation and EP for traits such as plant height, LDMC, LT, LNC and LCC. Plant height, LT, and LNC varied less along the elevational gradients in high EP species, whereas low EP plants showed greater variation in functional trait values (Fig.  1 ). Among them, plant height and LT of low EP species decreased at higher elevations, while LNC increased. On the contrary, LDMC of high EP species decreased gradually along the elevation, while that of low EP species remained at a low level. In addition, LCC was highest and lowest in median elevation for high and low EP species, respectively.

Relationship between functional traits and elevation, as influenced by species elevational preference (EP). Functional traits: Plant height, specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness (LT), leaf nitrogen content (LNC) and leaf carbon content (LCC). Shade areas are the 95% confidence intervals. cR 2 represents conditional R 2 value, and mR 2 represents marginal R 2 value. Trait values and elevation were standardized

The interactions of elevation and SR had significant effects on LDMC, LT, LNC and LCC. LT, LNC and LCC varied less along the elevational gradients in wide SR species, whereas narrow SR plants displayed a greater variation in functional trait values (Fig.  2 ). Wide and narrow species had opposite trends in LDMC. As the elevation increased, the LDMC of wide SR species decreased, and that of narrow SR species increased.

Relationship between functional traits and elevation, as influenced by species’ elevational range (SR). Functional traits: Plant height, specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness (LT), leaf nitrogen content (LNC) and leaf carbon content (LCC). Shade areas are the 95% confidence intervals. cR 2 represents conditional R 2 value, and mR 2 represents marginal R 2 value. Trait values and elevation were standardized

Species’ elevational distributions

Using EP as the horizontal axis and SR as the vertical axis, we plotted elevational distributions of desert species with observations greater than 10 times. The clustering of plant species with similar elevational distribution characteristics could be found in the Figure S9 . LN Group had 8 species and was characterized by low EP and narrow SR (0< EP<0.3, 0< SR<0.3), revealing that these species prefer lower elevations and only distributed at lower elevations. All species in this group were shrubs. We divided the 4 species into LW Group with low EP and wide SR (0< EP<0.3, 0.7< SR<1.0), indicating that these species prefer lower elevations but have a broader elevational distribution. This group consisted mainly of shrubs and forbs. Four species were classified into the HN group with high EP and narrow SR (0.7< EP<1.0, 0< SR<0.3), considering that these species were exclusive to high-elevation habitats. In this group, there were only two functional types (forbs and shrubs), and the species with the largest proportion were forbs. Ultimately, We classified 7 species into HW group with high EP and wide SR (0.7< EP<1.0, 0.7< SR<1.0), showing that these species prefer higher elevations but have a broader elevational distribution. In HW group, the most represented species were forbs and graminoids (Figure S9 ; Table S2 ).

Sources of variation in functional traits

We found that differences within species explained trait variation of plant height in low EP desert plants (Fig.  3 a). Both total and intraspecific variation in plant height decreased significantly with increasing EP, and the total variation decreased with increasing SR (Figure S10 a; Figure S11 a). The intraspecific variation of SLA and LDMC showed a high proportion of the total trait variation accounting for an average of 63.68% and 52.96% of total variation, respectively, and the total variation in SLA increased with increasing SR (Fig.  3 b c; Figure S11 b). LT had large interspecific variation, and total variation in LT decreased significantly with increasing EP and SR (Fig.  3 d; Figure S10 d; Figure S11 d). LNC of wide SR species had a large intraspecific variation, and interspecific variation in LNC increased with increasing EP (Fig.  3 e; Figure S10 e). Moreover, LCC in HW group exhibited large proportion of intraspecific variation, and interspecific variation in LCC decreased with increasing SR (Fig.  3 f; Figure S11 f).

Variance decomposition of height, specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness (LT), leaf nitrogen content (LNC) and leaf carbon content (LCC) measured across species with different elevational distributions. LN: species with low elevational preference and narrow species’ range; LW: species with low elevational preference and wide species’ range; HN: species with high elevational preference and narrow species’ range; HW: species with high elevational preference and wide species’ range. Colored bars show proportion of total trait variance (‘% trait Var’) while black bar shows absolute amount of variance (‘Tot trait Var’)

For species presenting these four distributions, we took into account patterns of intraspecific variation over elevation. We found that the significant negative feedback of plant height with elevation was reflected at the level of individual species for most species in all groups except the HW species group (Figure S12 a). SLA and LCC had significant intraspecific trends only for species in the LN and HW groups with the mostly decrease trend (Figure S12 b f). Most species with significant intraspecific variation trends in LDMC showed negative responses to elevation (Figure S12 c). LT of species in the LN group showed more constraint, with half of the species showing an increase trend and a quarter of the species a decrease trend (Figure S12 d). LNC showed the most consistent variance constraint with elevation in HW group, with half of the species showing higher trait values at higher elevation (Figure S12 e).

Relationships between functional traits and environmental factors

The relationship between functional traits of desert plants and environmental factors was further analyzed by RDA analysis, and the results showed that the cumulative explanation rates of the first two axes were 89.23%, 78.24%, 83.31% and 95.34%, respectively (Fig.  4 ). The first two axes could reflect the relationship well, and were mainly determined by axis I. In detail, temperature, precipitation and elevation provided a better explanation for the variation in functional traits in the LN group (Table S3 ). Environmental factors explain more about plant height, LCC and LT in the LN group. Temperature, elevation, precipitation, SNC, sand content, SCC and EC significantly affected ( p  < 0.05) the differences in functional traits of desert plants in the LW group. LCC, LNC and height of the LW group were better explained by environmental factors (Fig.  4 ; Table S3 ). In the HN group, temperature, elevation, sand content, SNC and pH were determined to be significant environmental factors ( p  < 0.05) affecting SLA, LNC and LCC of desert plants (Table S3 ). Temperature, elevation and sand content had significant effects on the variation of functional traits in the HW group, especially for SLA, LDMC and LNC.

Redundancy analysis of functional traits and environmental factors across species with different elevational distributions. Functional traits: plant height, specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness (LT), leaf nitrogen content (LNC) and leaf carbon content (LCC). Environmental factors: elevation, annual mean temperature (Temp), annual mean precipitation (Prec), soil pH (pH), soil electrical conductivity (EC), soil clay content (Clay), soil sand content (Sand), soil nitrogen content (SNC) and soil carbon content (SCC). LN: species with low elevational preference and narrow species’ range; LW: species with low elevational preference and wide species’ range; HN: species with high elevational preference and narrow species’ range; HW: species with high elevational preference and wide species’ range. Red lines indicate functional traits, and blue lines indicate environmental factors

Our results demonstrated that elevational distributions affected trait shifts. These shifts were manifested in both trait values and variance portioning. The significant value changes in plant height and LT over elevation were mainly driven by species with lower EP, while LDMC was mainly driven by species with higher EP. Higher EP species exhibit lower trait variation compared to lower EP species, which may have a reduced potential to respond to environmental gradients [ 1 ]. Moreover, intraspecific trait variations of species with different elevational distributions demonstrated different adaptation strategies corresponding to elevation changes. We provide preliminary evidence that elevation, temperature and precipitation were the main factors influencing functional traits in lower EP species, while higher EP species were not influenced by precipitation.

Variation in functional traits across a wide elevational gradient

A number of factors, including temperature, precipitation, solar radiation, and atmospheric pressure, can influence the patterns of functional traits along elevational gradients [ 26 ]. Plant height, LT, LNC and LCC significantly decreased with increasing elevation. These traits of plant growth and resource utilization indicate a survival strategy of desert plants along a wide elevational gradient. Variations in plant height are supported by previous studies [ 12 , 21 ]. Long documented, the negative correlation between plant height and elevation has been considered to be linked to community assembly and plastic variation of plants [ 12 , 27 ]. For example, plant communities at higher elevations have lower height compared to those at lower elevations [ 28 ]. Individuals of the same species at higher elevations tend to be shorter than those at lower elevations, according to homogeneous garden trials, indicating intraspecific adaptation of plant size to elevation [ 12 ].

Variations in leaf size along elevational gradients are determined by different climatic factors and soil conditions [ 29 ]. It is generally accepted that the leaves become smaller and thicker with increasing elevation resulting in low SLA, high LDMC and high LT [ 4 , 25 , 30 ]. This can be explained in terms of both water and heat. Firstly, because leaf size is a key factor in controlling evapotranspiration and is used as a proxy for energy and water balances [ 29 , 31 ], variations in leaf size caused by rising elevation may be a result of feedback from soil moisture [ 32 ]. In this experiment, both LT and SLA were strongly correlated with the precipitation. Secondly, variations in leaf size with elevation probably reflect the divergence in temperature between the day and night [ 4 ], with accumulation of matter in leaves [ 33 ]. Our results showed that the SLA in the LW group and the LDMC in the HW group were strongly related to temperature, which confirmed this opinion. Consequently, the variations in these traits may reflect the widespread and pervasive role of water-heat exchange in influencing plant plasticity [ 4 ]. However, our results suggested that the overall patterns of SLA and LDMC along elevation were not significant. This may be due to the complexity of trait variation within different species. Results from a study that measured SLA for 21 species at different elevational ranges found that SLA increased across elevational gradients [ 34 ]. This pattern may indicate that plants in higher elevations have lager photosynthetic capacity to ensure rapid carbon uptake [ 34 , 35 ].

In line with previous studies, LNC and LCC decreased with increasing elevation mainly due to temperature and precipitation decreased [ 13 , 36 ]. This result is consistent with the plant temperature-physiological theory, which postulates that plants’ metabolic activity slows down in the cold [ 12 ]. Transpiration, along with feedback from the soil and atmosphere, is the primary factor influencing changes in leaf nutrient concentrations [ 37 ]. These directional shifts in traits along elevational gradients collectively suggest that plants adjust their height, leaf size and nutrient concentration to make up for decreased photosynthetic capacity in response to lower temperatures, reduced precipitation, rising solar radiation and increased atmospheric pressure [ 21 ].

Species occupying different elevational distributions

Different plants have different elevational preferences, which is partially reflected in their spatial distribution [ 1 ]. The EP and SR of desert species were considered as X and Y axes, and we identified four categories of desert plants. Species in LN group characterized by low EP and narrow SR, contain Alhagi camelorum , Anabasis brevifolia , Halostachys caspica , Haloxylon ammodendron , Kalidium foliatum , Nitraria tangutorum , Tamarix chinensis and Zygophyllum xanthoxylum . These species, mostly drought and salinity tolerant plants, usually have small and tough leaves to conserve water and prevent transpiration [ 38 ], thus tending to colonize in specific areas, such as the saline and arid lands. LW group contains drought-tolerant species, and it is also characterized by a high resistance to harsh environments and high reproductive capacity, thus having a relatively wide distribution. Species in HN group with high EP and narrow SR, contain Asteraceae wellbyi , Christolea crassifolia , Oxytropis microphylla and Stellera chamaejasme . These species are cold tolerant and grow at higher elevations. Especially, Asteraceae wellbyi is endemic to Tibet. Species in the HW group are highly adaptable and barrenness tolerant. The wide distribution may be due to their high vigor and diverse reproduction modes, which enable them to occupy the ground quickly, as well as a well-developed fibrous root system and some physiological and ecological characteristics essential for adaptation to environmental stress [ 39 ].

Trait variation in species with different elevational distributions

For species that inhabit various elevational distributions, we would anticipate different trait values and variation portioning. First of all, we considered functional trait values over elevation for species with different elevational distributions. Our study suggested that the patterns of trait change with elevation depended on the EP and SR. In particular, the main reason for the decrease in plant height and LT with elevation is the species with a low EP. In contrast, elevation-induced LDMC reduction was primarily caused by high EP species. These results suggest that species with different elevational preferences may have different strategies for functional trait variation in response to environmental changes. Desert plants with a high EP may be subject to more abiotic stressors and less interspecific competition than species with a low EP, which may help them stick to their conservative growth strategy of staying small [ 1 ]. Desert plants with a low EP tend to be shrubs with succulent leaves that generally have lower LDMC to withstand drought [ 25 , 40 ]. Most importantly, the inconsistency in the relationship between LDMC and elevation may stem from the species with different SR. In a narrow SR, the changes in LDMC were consistent with most studies [ 4 , 25 , 30 ], suggesting that hydrothermal conditions play an important role in the trait response process [ 4 , 32 ]. Over a wide SR, however, variation in LDMC may be caused primarily by characteristics of different species. As mentioned before, species of LN group are mainly drought-tolerant shrubs and salt plants, and species of HN group are mainly cold-tolerant graminoids.

Trait variation across elevational gradients may also be a means by which desert species convey their varying preferences for habitat. We offered preliminary proof that desert species with various elevational distributions have diverse patterns of trait variation portioning. According to our results, structural traits of high EP plants show relatively little variation with elevation, which may point to a higher capacity for adapting to environmental changes [ 1 , 34 , 41 ]. While species with a high EP show greater interspecific variation in nutrient trait values with elevation. Given the potential effects of climate change, plant species with relatively high trait variability may be more adaptable to different environmental situations than those with relatively low trait variability [ 1 ]. Moreover, intraspecific variations in LNC and LCC were higher in species with a wide SR than in species with a narrow SR, highlighting the high trait plasticity of plant carbon and nitrogen content in widely distributed species. The global mean value of intraspecific variation was 25% [ 42 ], and the contribution of intraspecific variation to total trait variation was either equal to or greater than this value for the different groups in this study, despite tough environmental conditions and wide species ranges in Tibetan Plateau. However, intraspecific trait variation varied considerably for different traits, e.g., intraspecific trait variation in SLA and LDMC accounted for about 50% of the total variation, which was consistent with previous studies [ 30 ]. Our results provide evidence that the distribution of species along environmental gradients is constrained by intraspecific trait variation [ 34 ]. Taken together, the results of this study revealed that, plant establishment and adaption success under varying environmental conditions can be attributed to differences in functional traits [ 6 , 29 ].

Response of functional traits to environmental factors

In addition to the influence of the species’ elevational distributions of the plant itself, external environmental factors are important for variation in plant functional traits. Temperature and precipitation are important determinants of the regional climate type and significantly affect plant growth and development. We discovered that species with a high EP faced constraints from elevation and temperature, those with a low EP mostly derived their functional traits from elevation temperature and precipitation. Species with high EP are mostly located in the alpine desert of the Tibetan plateau, where the presence of cold climatic conditions and permafrost prevent plants from efficiently utilizing water [ 43 ]. Therefore, changes in the functional traits of desert plants are not significantly influenced by precipitation in this area. Our results suggest that LNC was negatively correlated with temperature, which is consistent with previous studies in field surveys and simulated controlled experiments [ 44 , 45 ]. This may because that high nitrogen content at low temperatures is needed to compensate for the reduced biochemical efficiency caused by the reduction of high-nitrogen enzyme activity [ 46 ]. Alternatively, warmer climate accelerates the plant growth process, thus diluting LNC [ 45 ]. Precipitation negatively correlates with SLA for low EP species, suggesting the plant adaptation strategies to maximize carbon income and minimize water consumption under drought stress [ 4 , 47 ]. In this study, soil properties such as sand content and SNC have important effects on the formation of functional traits in desert plants, as soil is a material and energy source for plant growth and development.

Conclusions

We discovered that desert plant species displayed different trait trends over elevation, and that these associations relied on the elevational distributions (elevational preferences and species’ ranges) of the individual species. In particular, species with lower elevational preferences expressed higher trait variation in structure trait than those with higher elevational preferences. It was suggested by the increased intraspecific variation of SLA and LDMC that these species may be better adapted to biotic and abiotic changes. Plant species with lower elevational ranges have trait-elevation connections that are widely applicable globally, but LDMC at wider elevational ranges show opposite trends, suggesting that interspecific variation plays an important role in size-related traits at large scales. Most importantly, the main controlling factors of functional traits differed among species with different elevational distributions. Our experiments provide preliminary evidence that desert species with different elevational distributions have different trait distribution patterns and adaptation mechanisms.

Data availability

Data is provided within the manuscript or supplementary information files.

Abbreviations

Electrical conductivity

Elevational preference

Leaf carbon content

Leaf dry matter content

Leaf nitrogen content

Leaf thickness

Redundancy analysis

Soil carbon content

Specific leaf area

Soil nitrogen content

Species’ range

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This work was financially supported by the Second Tibetan Plateau Scientific Expedition and Research program (No. 2019QZKK0305), National Natural Science Foundation of China (No. 42071140) and CAS “Light of West China” Program (No. E129050301).

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Hu, Y., Li, X., Wang, S. et al. Patterns and driving factors of functional traits of desert species with different elevational distributions in the Tibetan Plateau and adjacent areas. BMC Plant Biol 24 , 371 (2024). https://doi.org/10.1186/s12870-024-05080-x

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People Who Typically Lack Empathy Have These 20 Traits

Posted: May 12, 2024 | Last updated: May 12, 2024

Empathy, the capacity to comprehend and empathize with another’s emotions, stands as a core attribute that enhances connectivity, dialogue, and empathy within our interpersonal bonds. Nevertheless, this trait is not uniformly present in everyone. Individuals deficient in empathy may often pose difficulties in social engagements and personal relationships. Identifying the attributes of those who find empathy challenging is vital for better comprehension and management of our interactions with them. In this exploration, we will uncover 25 traits frequently linked to an absence of empathy, illuminating the ways in which these characteristics appear and influence our relationships with others.

Poor Listening Skills

Listening plays an essential role in empathy, enabling us to grasp and resonate with the experiences of others. However, individuals with low empathy frequently fall short in listening attentively, obstructing significant exchanges. Their tendency to concentrate on their own ideas or to bide their time until it’s their turn to speak undermines the possibility of meaningful dialogue. As a result, others may feel disregarded and devalued, erecting obstacles to comprehension and conflict resolution. This issue makes it challenging for relationships to develop and flourish.

Disregard for Social Norms

Social norms and etiquette, fundamentally reliant on mutual respect and comprehension, are concepts that those lacking empathy might overlook, resulting in behaviors that can be socially awkward or offensive. The neglect of these norms can manifest in actions perceived as rude, offensive, or inappropriate by others, due to the individual’s failure to acknowledge or concern themselves with the social and emotional repercussions of their actions. This indifference can lead to alienation and erect obstacles to social integration and acceptance.

Lack of Guilt or Remorse

Experiencing guilt or remorse following harmful actions is inherently linked to empathy, an emotion that may be missing in individuals with low levels of empathy, prompting them to persist in damaging behaviors unaware of the necessity for modification. Absent this capacity, individuals might not sincerely regret the outcomes of their actions, resulting in a cycle of repeated harm without any real learning or development. This deficiency in remorse complicates the ability of others to trust or forgive, with the understanding that harmful conduct could recur with ease.

Competitiveness at the Expense of Others

Although competition can foster growth, individuals with a deficit in empathy may seek triumph or achievement with little concern for the adverse effects on others, frequently rationalizing their behavior as part of the competitive process or as essential for success. Their primary aim is to win or succeed at any expense, neglecting principles of fairness or the welfare of their rivals. Such an attitude can strain relationships and cultivate a contentious atmosphere, whether in professional settings, personal interactions, or social contexts.

Avoidance of Emotional Topics

People exhibiting low empathy tend to avoid conversations centered on feelings or emotions, deeming them either uncomfortable or insignificant, thereby bypassing chances for a more profound rapport. This reluctance complicates their ability to form deeper connections or partake in dialogues that demand emotional transparency. Consequently, relationships may suffer from a dearth of emotional depth, causing partners or friends to experience feelings of dissatisfaction and detachment.

Unwillingness to Help Others

Empathetic individuals are frequently inspired to aid those in distress, fueled by a collective sense of human connection and insight. However, the lack of empathy might lead to apathy concerning the hardships of others. Such a person may display minimal enthusiasm in providing support, except when it serves their immediate interests, illustrating a quid pro quo mindset in dealings that erodes the essence of communal solidarity and reciprocal assistance.

Intolerance to Differing Viewpoints

Empathy nurtures an appreciation for and tolerance of diverse viewpoints, but lacking it can lead to a rigid attitude against any differing perspectives. In its absence, there’s a propensity to consider one’s own opinions as paramount, giving rise to disputes and a failure to recognize the value of diverse insights and experiences. Such intolerance can segregate individuals and communities, fostering echo chambers that hinder both personal and communal development.

Inability to Apologize Sincerely

Authentic apologies entail recognizing the effect one’s actions have on others and experiencing regret, a mechanism intrinsically linked to empathy, which some individuals lack. Those deficient in empathy may extend insincere apologies that evade embracing true accountability, assuming they apologize at all. This aversion to offering heartfelt apologies can hinder the mending of relational rifts, allowing unresolved matters to deteriorate and trust to diminish gradually.

Superficial Charm

Charm, when employed by those deficient in profound empathy, can serve as a tactical means to maneuver through social scenarios or attain particular aims without establishing authentic bonds, frequently resulting in feelings of exploitation among those affected once the allure dissipates. This type of charm usually lacks substance, functioning more as a mechanism than a manifestation of sincere interest or warmth. The ephemeral quality of this charm can cause feelings of disenchantment and distrust among those initially captivated by it, upon recognizing the absence of a real connection.

Inflexibility in Thinking

A flexible mindset enables the exploration of different viewpoints and solutions, yet the absence of empathy may foster inflexible thinking, which obstructs constructive conversations and problem resolution. Devoid of empathy, thought processes tend to solidify, marked by an unwillingness to look past personal perspectives or to entertain different problem-solving methods. Such rigidity can impede both personal and professional development, along with the capacity to adjust to novel scenarios or to settle disputes efficiently.

Unwillingness to Change or Self-Reflect

Self-reflection and a readiness to evolve typically stem from an awareness of how our actions affect those around us, yet in the absence of empathy, the incentive for such introspection significantly dwindles. Lacking empathy, individuals may find scant reason to engage in self-examination or alter their conduct, even when confronted with adverse results or criticism. This aversion to modification can stifle personal progression and maturation, trapping individuals in detrimental behavioral cycles that not only harm themselves but also negatively impact others.

Dominating Conversations

Monopolizing discussions without allowing others room to contribute signifies a deficit in empathy and an indifference to the importance of dialogue and exchange. It reveals a lack of appreciation for the contributions and viewpoints of others, rendering equitable and substantive interactions challenging. Such dominance can suppress the voices of others, resulting in a skewed relationship where authentic exchange and comprehension are scarce.

Quick to Anger

A short fuse may signal a lack of capacity to grasp and empathize with the intentions or emotions of others, prompting overblown responses to trivial matters. Irritation mounts swiftly in someone unable to comprehend or sympathize with differing viewpoints or feelings, resulting in rapid and vehement outbursts of anger over minor provocations or conflicts. This unpredictability fosters an atmosphere of stress and discomfort, discouraging frank dialogue and cooperative efforts.

Unrealistic Expectations of Others

Imposing unattainable expectations on others, neglecting their abilities, conditions, or emotions, frequently results in ongoing disillusionment and irritation, exerting excessive strain on interpersonal connections. In the absence of empathy to acknowledge others’ restrictions and hurdles, a person might maintain impractical anticipations, culminating in disenchantment and strife. Such demands can burden relationships, leaving others feeling incapable of fulfilling these expectations and disheartened by the absence of comprehension and encouragement.

Misinterpreting Social Cues

Empathy plays a crucial role in correctly deciphering social signals, yet a lack thereof can provoke clumsy or unsuitable reactions, fueling social misinterpretations and discord. Insufficient empathy can cause the misinterpretation of cues, paving the way for misunderstandings and miscommunications. This impairment can obstruct efficient communication and the formation of relationships, as individuals may feel neglected or misconstrued.

Reluctance to Compromise

Compromise is predicated on the acknowledgment and appreciation of another person’s viewpoint, yet a hesitancy to engage in this process can arise from a lack of empathy, resulting in inflexible positions that obstruct agreement and mutual comprehension. Individuals with low empathy may struggle to concede or find common ground, preferring to adhere to their perspectives. This stubbornness can culminate in persistent disagreements and a deterioration of dialogue, leaving all involved feeling disregarded and devalued, thus eroding the trust and collaboration essential for nurturing relationships.

Insensitive Jokes or Comments

Using humor at the expense of others’ feelings or sensitivities indicates a deficiency in empathy, potentially leading to unintended offense and estrangement. Making light of situations or commenting without pondering the impact on others can result in wounded sentiments and damaged relationships. This lack of sensitivity can isolate people and foster an environment of disrespect, eroding the sense of security and inclusiveness essential for thriving communities.

Self-Centeredness

People with a pronounced focus on themselves tend to neglect the needs and emotions of others, hindering their ability to participate in mutual relationships. This self-absorbed behavior can lead to estrangement from friends and family, who might feel overlooked or underappreciated as their own needs and feelings are repeatedly disregarded. The incapacity to look beyond one’s own viewpoint severely restricts the depth and richness of personal interactions.

Difficulty with Emotional Support

Individuals deficient in empathy struggle to extend comfort or comprehension during moments of need, frequently seeming detached or clumsy in situations laden with emotion. They might find it challenging to react suitably to the emotional turmoil of others, opting to propose solutions rather than express empathy or choosing to evade these scenarios entirely. This lack of emotional support can stress relationships, causing loved ones to feel abandoned during their times of emotional requirement and doubting the solidity of their connection.

Impatience with Others’ Problems

A deficiency in patience for the troubles of others marks a significant indicator of low empathy, resulting in these individuals minimizing or belittling the worries of others. They might regard conversations on issues that don’t personally impact them as boring or unwarranted. This impatience can surface through dismissive remarks, inattentiveness, or even annoyance, thereby further inhibiting open dialogue and exacerbating the emotional rift with others.

Blaming Others for Misfortunes

In the absence of empathy, individuals seldom take accountability for their actions during disputes or misunderstandings, finding it simpler to fault others without reflecting on their own contribution to the issue. Rather than acknowledging the intricate dynamics of actions and responses that fuel situations, they may place the blame solely on others, dodging personal responsibility. This tendency to shift blame undermines trust and impedes conflict resolution, further entrenching cycles of miscommunication and bitterness.

Manipulative Behavior

Manipulation entails exploiting others for personal advantage, disregarding their emotions or welfare—a tactic easily adopted by individuals devoid of empathy, without ethical reservations. They may resort to dishonesty, allure, or pressure to fulfill their objectives, perceiving others more as tools to an end rather than beings with inherent rights and emotions. This approach fosters relationships grounded in duplicity and exploitation, proving ultimately unviable and harmful to everyone concerned.

Difficulty in Maintaining Close Relationships

Empathy facilitates the creation of profound, significant bonds through the appreciation and comprehension of others’ emotions and viewpoints, an essential element absent in individuals with low empathy. In its absence, relationships often lack substance and emotional closeness, resulting in shallow connections prone to deterioration amid tension or disagreement. Consequently, forging and preserving intimate, enduring relationships capable of withstanding adversities becomes a daunting task.

Emotional Coldness

An individual perceived as emotionally detached or aloof might grapple with empathy, rendering it difficult for others to forge a significant emotional bond with them. They may encounter challenges in articulating their own emotions or in responding to the emotions of others with warmth and empathy, which complicates emotional engagement. This apparent emotional detachment can create an enduring perception of disconnection and distance, repelling others and obstructing the development of intimate connections.

Overly Critical Attitude

The absence of empathy may lead to an emphasis on the shortcomings or errors of others, neglecting to acknowledge their endeavors or the circumstances surrounding their actions, which can have a disheartening effect on those targeted. This critical approach can demoralize and injure those in proximity, impairing relationships and diminishing self-worth. Persistent criticism can erode the resilience and self-assurance of individuals, making it challenging for them to communicate freely or participate in reciprocal development.

Recognizing these characteristics aims not to stigmatize or criticize individuals facing challenges with empathy but to cultivate consciousness and kindness in our dealings. Empathy is a competence that can be nurtured over time through dedication and perseverance. By identifying indicators of its scarcity, we can more adeptly manage our relationships, provide assistance where necessary, and strive for connections imbued with empathy. It’s crucial to approach this matter with delicacy and an open heart, bearing in mind that everyone’s potential for empathy can expand through insight and exercise.

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