scientific method definition essay

Scientific Method

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The scientific method is a series of steps followed by scientific investigators to answer specific questions about the natural world. It involves making observations, formulating a hypothesis , and conducting scientific experiments . Scientific inquiry starts with an observation followed by the formulation of a question about what has been observed. The steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. This is very important if you are doing a science project because you want your project to be focused on something that will hold your attention. Your observation can be on anything from plant movement to animal behavior, as long as it is something you really want to know more about.​ This is where you come up with the idea for your science project.

Once you've made your observation, you must formulate a question about what you have observed. Your question should tell what it is that you are trying to discover or accomplish in your experiment. When stating your question you should be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question may be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you've developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states very clearly how you plan to conduct your experiment. It is important that you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

The final step of the scientific method is developing a conclusion. This is where all of the results from the experiment are analyzed and a determination is reached about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

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What is the Scientific Method: How does it work and why is it important?

The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA. This ongoing approach promotes reason, evidence, and the pursuit of truth in science.

Updated on November 18, 2023

Beginning in elementary school, we are exposed to the scientific method and taught how to put it into practice. As a tool for learning, it prepares children to think logically and use reasoning when seeking answers to questions.

Rather than jumping to conclusions, the scientific method gives us a recipe for exploring the world through observation and trial and error. We use it regularly, sometimes knowingly in academics or research, and sometimes subconsciously in our daily lives.

In this article we will refresh our memories on the particulars of the scientific method, discussing where it comes from, which elements comprise it, and how it is put into practice. Then, we will consider the importance of the scientific method, who uses it and under what circumstances.

What is the scientific method?

The scientific method is a dynamic process that involves objectively investigating questions through observation and experimentation . Applicable to all scientific disciplines, this systematic approach to answering questions is more accurately described as a flexible set of principles than as a fixed series of steps.

The following representations of the scientific method illustrate how it can be both condensed into broad categories and also expanded to reveal more and more details of the process. These graphics capture the adaptability that makes this concept universally valuable as it is relevant and accessible not only across age groups and educational levels but also within various contexts.

Steps in the scientific method

While the scientific method is versatile in form and function, it encompasses a collection of principles that create a logical progression to the process of problem solving:

• Define a question : Constructing a clear and precise problem statement that identifies the main question or goal of the investigation is the first step. The wording must lend itself to experimentation by posing a question that is both testable and measurable.
• Gather information and resources : Researching the topic in question to find out what is already known and what types of related questions others are asking is the next step in this process. This background information is vital to gaining a full understanding of the subject and in determining the best design for experiments. 
• Form a hypothesis : Composing a concise statement that identifies specific variables and potential results, which can then be tested, is a crucial step that must be completed before any experimentation. An imperfection in the composition of a hypothesis can result in weaknesses to the entire design of an experiment.
• Perform the experiments : Testing the hypothesis by performing replicable experiments and collecting resultant data is another fundamental step of the scientific method. By controlling some elements of an experiment while purposely manipulating others, cause and effect relationships are established.
• Analyze the data : Interpreting the experimental process and results by recognizing trends in the data is a necessary step for comprehending its meaning and supporting the conclusions. Drawing inferences through this systematic process lends substantive evidence for either supporting or rejecting the hypothesis.
• Report the results : Sharing the outcomes of an experiment, through an essay, presentation, graphic, or journal article, is often regarded as a final step in this process. Detailing the project's design, methods, and results not only promotes transparency and replicability but also adds to the body of knowledge for future research.
• Retest the hypothesis : Repeating experiments to see if a hypothesis holds up in all cases is a step that is manifested through varying scenarios. Sometimes a researcher immediately checks their own work or replicates it at a future time, or another researcher will repeat the experiments to further test the hypothesis.

Where did the scientific method come from?

Oftentimes, ancient peoples attempted to answer questions about the unknown by:

• Making simple observations
• Discussing the possibilities with others deemed worthy of a debate
• Drawing conclusions based on dominant opinions and preexisting beliefs

For example, take Greek and Roman mythology. Myths were used to explain everything from the seasons and stars to the sun and death itself.

However, as societies began to grow through advancements in agriculture and language, ancient civilizations like Egypt and Babylonia shifted to a more rational analysis for understanding the natural world. They increasingly employed empirical methods of observation and experimentation that would one day evolve into the scientific method . 

In the 4th century, Aristotle, considered the Father of Science by many, suggested these elements , which closely resemble the contemporary scientific method, as part of his approach for conducting science:

• Study what others have written about the subject.
• Look for the general consensus about the subject.
• Perform a systematic study of everything even partially related to the topic.

By continuing to emphasize systematic observation and controlled experiments, scholars such as Al-Kindi and Ibn al-Haytham helped expand this concept throughout the Islamic Golden Age . 

In his 1620 treatise, Novum Organum , Sir Francis Bacon codified the scientific method, arguing not only that hypotheses must be tested through experiments but also that the results must be replicated to establish a truth. Coming at the height of the Scientific Revolution, this text made the scientific method accessible to European thinkers like Galileo and Isaac Newton who then put the method into practice.

As science modernized in the 19th century, the scientific method became more formalized, leading to significant breakthroughs in fields such as evolution and germ theory. Today, it continues to evolve, underpinning scientific progress in diverse areas like quantum mechanics, genetics, and artificial intelligence.

Why is the scientific method important?

The history of the scientific method illustrates how the concept developed out of a need to find objective answers to scientific questions by overcoming biases based on fear, religion, power, and cultural norms. This still holds true today.

By implementing this standardized approach to conducting experiments, the impacts of researchers’ personal opinions and preconceived notions are minimized. The organized manner of the scientific method prevents these and other mistakes while promoting the replicability and transparency necessary for solid scientific research.

The importance of the scientific method is best observed through its successes, for example: 

• “ Albert Einstein stands out among modern physicists as the scientist who not only formulated a theory of revolutionary significance but also had the genius to reflect in a conscious and technical way on the scientific method he was using.” Devising a hypothesis based on the prevailing understanding of Newtonian physics eventually led Einstein to devise the theory of general relativity .
• Howard Florey “Perhaps the most useful lesson which has come out of the work on penicillin has been the demonstration that success in this field depends on the development and coordinated use of technical methods.” After discovering a mold that prevented the growth of Staphylococcus bacteria, Dr. Alexander Flemimg designed experiments to identify and reproduce it in the lab, thus leading to the development of penicillin .
• James D. Watson “Every time you understand something, religion becomes less likely. Only with the discovery of the double helix and the ensuing genetic revolution have we had grounds for thinking that the powers held traditionally to be the exclusive property of the gods might one day be ours. . . .” By using wire models to conceive a structure for DNA, Watson and Crick crafted a hypothesis for testing combinations of amino acids, X-ray diffraction images, and the current research in atomic physics, resulting in the discovery of DNA’s double helix structure .

Final thoughts

As the cases exemplify, the scientific method is never truly completed, but rather started and restarted. It gave these researchers a structured process that was easily replicated, modified, and built upon. 

While the scientific method may “end” in one context, it never literally ends. When a hypothesis, design, methods, and experiments are revisited, the scientific method simply picks up where it left off. Each time a researcher builds upon previous knowledge, the scientific method is restored with the pieces of past efforts.

By guiding researchers towards objective results based on transparency and reproducibility, the scientific method acts as a defense against bias, superstition, and preconceived notions. As we embrace the scientific method's enduring principles, we ensure that our quest for knowledge remains firmly rooted in reason, evidence, and the pursuit of truth.

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1.3: The Scientific Method - How Chemists Think

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Learning Objectives

• Identify the components of the scientific method.

Scientists search for answers to questions and solutions to problems by using a procedure called the scientific method. This procedure consists of making observations, formulating hypotheses, and designing experiments; which leads to additional observations, hypotheses, and experiments in repeated cycles (Figure \(\PageIndex{1}\)).

Step 1: Make observations

Observations can be qualitative or quantitative. Qualitative observations describe properties or occurrences in ways that do not rely on numbers. Examples of qualitative observations include the following: "the outside air temperature is cooler during the winter season," "table salt is a crystalline solid," "sulfur crystals are yellow," and "dissolving a penny in dilute nitric acid forms a blue solution and a brown gas." Quantitative observations are measurements, which by definition consist of both a number and a unit. Examples of quantitative observations include the following: "the melting point of crystalline sulfur is 115.21° Celsius," and "35.9 grams of table salt—the chemical name of which is sodium chloride—dissolve in 100 grams of water at 20° Celsius." For the question of the dinosaurs’ extinction, the initial observation was quantitative: iridium concentrations in sediments dating to 66 million years ago were 20–160 times higher than normal.

Step 2: Formulate a hypothesis

After deciding to learn more about an observation or a set of observations, scientists generally begin an investigation by forming a hypothesis, a tentative explanation for the observation(s). The hypothesis may not be correct, but it puts the scientist’s understanding of the system being studied into a form that can be tested. For example, the observation that we experience alternating periods of light and darkness corresponding to observed movements of the sun, moon, clouds, and shadows is consistent with either one of two hypotheses:

• Earth rotates on its axis every 24 hours, alternately exposing one side to the sun.
• The sun revolves around Earth every 24 hours.

Suitable experiments can be designed to choose between these two alternatives. For the disappearance of the dinosaurs, the hypothesis was that the impact of a large extraterrestrial object caused their extinction. Unfortunately (or perhaps fortunately), this hypothesis does not lend itself to direct testing by any obvious experiment, but scientists can collect additional data that either support or refute it.

Step 3: Design and perform experiments

After a hypothesis has been formed, scientists conduct experiments to test its validity. Experiments are systematic observations or measurements, preferably made under controlled conditions—that is—under conditions in which a single variable changes.

Step 4: Accept or modify the hypothesis

A properly designed and executed experiment enables a scientist to determine whether or not the original hypothesis is valid. If the hypothesis is valid, the scientist can proceed to step 5. In other cases, experiments often demonstrate that the hypothesis is incorrect or that it must be modified and requires further experimentation.

Step 5: Development into a law and/or theory

More experimental data are then collected and analyzed, at which point a scientist may begin to think that the results are sufficiently reproducible (i.e., dependable) to merit being summarized in a law, a verbal or mathematical description of a phenomenon that allows for general predictions. A law simply states what happens; it does not address the question of why.

One example of a law, the law of definite proportions , which was discovered by the French scientist Joseph Proust (1754–1826), states that a chemical substance always contains the same proportions of elements by mass. Thus, sodium chloride (table salt) always contains the same proportion by mass of sodium to chlorine, in this case 39.34% sodium and 60.66% chlorine by mass, and sucrose (table sugar) is always 42.11% carbon, 6.48% hydrogen, and 51.41% oxygen by mass.

Whereas a law states only what happens, a theory attempts to explain why nature behaves as it does. Laws are unlikely to change greatly over time unless a major experimental error is discovered. In contrast, a theory, by definition, is incomplete and imperfect, evolving with time to explain new facts as they are discovered.

Because scientists can enter the cycle shown in Figure \(\PageIndex{1}\) at any point, the actual application of the scientific method to different topics can take many different forms. For example, a scientist may start with a hypothesis formed by reading about work done by others in the field, rather than by making direct observations.

Example \(\PageIndex{1}\)

Classify each statement as a law, a theory, an experiment, a hypothesis, an observation.

• Ice always floats on liquid water.
• Birds evolved from dinosaurs.
• Hot air is less dense than cold air, probably because the components of hot air are moving more rapidly.
• When 10 g of ice were added to 100 mL of water at 25°C, the temperature of the water decreased to 15.5°C after the ice melted.
• The ingredients of Ivory soap were analyzed to see whether it really is 99.44% pure, as advertised.
• This is a general statement of a relationship between the properties of liquid and solid water, so it is a law.
• This is a possible explanation for the origin of birds, so it is a hypothesis.
• This is a statement that tries to explain the relationship between the temperature and the density of air based on fundamental principles, so it is a theory.
• The temperature is measured before and after a change is made in a system, so these are observations.
• This is an analysis designed to test a hypothesis (in this case, the manufacturer’s claim of purity), so it is an experiment.

Exercise \(\PageIndex{1}\) 

Classify each statement as a law, a theory, an experiment, a hypothesis, a qualitative observation, or a quantitative observation.

• Measured amounts of acid were added to a Rolaids tablet to see whether it really “consumes 47 times its weight in excess stomach acid.”
• Heat always flows from hot objects to cooler ones, not in the opposite direction.
• The universe was formed by a massive explosion that propelled matter into a vacuum.
• Michael Jordan is the greatest pure shooter to ever play professional basketball.
• Limestone is relatively insoluble in water, but dissolves readily in dilute acid with the evolution of a gas.

The scientific method is a method of investigation involving experimentation and observation to acquire new knowledge, solve problems, and answer questions. The key steps in the scientific method include the following:

• Step 1: Make observations.
• Step 2: Formulate a hypothesis.
• Step 3: Test the hypothesis through experimentation.
• Step 4: Accept or modify the hypothesis.
• Step 5: Develop into a law and/or a theory.

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General Education

When you’re faced with a scientific problem, solving it can seem like an impossible prospect. There are so many possible explanations for everything we see and experience—how can you possibly make sense of them all? Science has a simple answer: the scientific method.

The scientific method is a method of asking and answering questions about the world. These guiding principles give scientists a model to work through when trying to understand the world, but where did that model come from, and how does it work?

In this article, we’ll define the scientific method, discuss its long history, and cover each of the scientific method steps in detail.

What Is the Scientific Method?

At its most basic, the scientific method is a procedure for conducting scientific experiments. It’s a set model that scientists in a variety of fields can follow, going from initial observation to conclusion in a loose but concrete format.

The number of steps varies, but the process begins with an observation, progresses through an experiment, and concludes with analysis and sharing data. One of the most important pieces to the scientific method is skepticism —the goal is to find truth, not to confirm a particular thought. That requires reevaluation and repeated experimentation, as well as examining your thinking through rigorous study.

There are in fact multiple scientific methods, as the basic structure can be easily modified.  The one we typically learn about in school is the basic method, based in logic and problem solving, typically used in “hard” science fields like biology, chemistry, and physics. It may vary in other fields, such as psychology, but the basic premise of making observations, testing, and continuing to improve a theory from the results remain the same.

The History of the Scientific Method

The scientific method as we know it today is based on thousands of years of scientific study. Its development goes all the way back to ancient Mesopotamia, Greece, and India.

The Ancient World

In ancient Greece, Aristotle devised an inductive-deductive process , which weighs broad generalizations from data against conclusions reached by narrowing down possibilities from a general statement. However, he favored deductive reasoning, as it identifies causes, which he saw as more important.

Aristotle wrote a great deal about logic and many of his ideas about reasoning echo those found in the modern scientific method, such as ignoring circular evidence and limiting the number of middle terms between the beginning of an experiment and the end. Though his model isn’t the one that we use today, the reliance on logic and thorough testing are still key parts of science today.

The Middle Ages

The next big step toward the development of the modern scientific method came in the Middle Ages, particularly in the Islamic world. Ibn al-Haytham, a physicist from what we now know as Iraq, developed a method of testing, observing, and deducing for his research on vision. al-Haytham was critical of Aristotle’s lack of inductive reasoning, which played an important role in his own research.

Other scientists, including Abū Rayhān al-Bīrūnī, Ibn Sina, and Robert Grosseteste also developed models of scientific reasoning to test their own theories. Though they frequently disagreed with one another and Aristotle, those disagreements and refinements of their methods led to the scientific method we have today.

Following those major developments, particularly Grosseteste’s work, Roger Bacon developed his own cycle of observation (seeing that something occurs), hypothesis (making a guess about why that thing occurs), experimentation (testing that the thing occurs), and verification (an outside person ensuring that the result of the experiment is consistent).

After joining the Franciscan Order, Bacon was granted a special commission to write about science; typically, Friars were not allowed to write books or pamphlets. With this commission, Bacon outlined important tenets of the scientific method, including causes of error, methods of knowledge, and the differences between speculative and experimental science. He also used his own principles to investigate the causes of a rainbow, demonstrating the method’s effectiveness.

Scientific Revolution

Throughout the Renaissance, more great thinkers became involved in devising a thorough, rigorous method of scientific study. Francis Bacon brought inductive reasoning further into the method, whereas Descartes argued that the laws of the universe meant that deductive reasoning was sufficient. Galileo’s research was also inductive reasoning-heavy, as he believed that researchers could not account for every possible variable; therefore, repetition was necessary to eliminate faulty hypotheses and experiments.

All of this led to the birth of the Scientific Revolution , which took place during the sixteenth and seventeenth centuries. In 1660, a group of philosophers and physicians joined together to work on scientific advancement. After approval from England’s crown , the group became known as the Royal Society, which helped create a thriving scientific community and an early academic journal to help introduce rigorous study and peer review.

Previous generations of scientists had touched on the importance of induction and deduction, but Sir Isaac Newton proposed that both were equally important. This contribution helped establish the importance of multiple kinds of reasoning, leading to more rigorous study.

As science began to splinter into separate areas of study, it became necessary to define different methods for different fields. Karl Popper was a leader in this area—he established that science could be subject to error, sometimes intentionally. This was particularly tricky for “soft” sciences like psychology and social sciences, which require different methods. Popper’s theories furthered the divide between sciences like psychology and “hard” sciences like chemistry or physics.

Paul Feyerabend argued that Popper’s methods were too restrictive for certain fields, and followed a less restrictive method hinged on “anything goes,” as great scientists had made discoveries without the Scientific Method. Feyerabend suggested that throughout history scientists had adapted their methods as necessary, and that sometimes it would be necessary to break the rules. This approach suited social and behavioral scientists particularly well, leading to a more diverse range of models for scientists in multiple fields to use.

The Scientific Method Steps

Though different fields may have variations on the model, the basic scientific method is as follows:

#1: Make Observations 

Notice something, such as the air temperature during the winter, what happens when ice cream melts, or how your plants behave when you forget to water them.

#2: Ask a Question

Turn your observation into a question. Why is the temperature lower during the winter? Why does my ice cream melt? Why does my toast always fall butter-side down?

This step can also include doing some research. You may be able to find answers to these questions already, but you can still test them!

#3: Make a Hypothesis

A hypothesis is an educated guess of the answer to your question. Why does your toast always fall butter-side down? Maybe it’s because the butter makes that side of the bread heavier.

A good hypothesis leads to a prediction that you can test, phrased as an if/then statement. In this case, we can pick something like, “If toast is buttered, then it will hit the ground butter-first.”

#4: Experiment

Your experiment is designed to test whether your predication about what will happen is true. A good experiment will test one variable at a time —for example, we’re trying to test whether butter weighs down one side of toast, making it more likely to hit the ground first.

The unbuttered toast is our control variable. If we determine the chance that a slice of unbuttered toast, marked with a dot, will hit the ground on a particular side, we can compare those results to our buttered toast to see if there’s a correlation between the presence of butter and which way the toast falls.

If we decided not to toast the bread, that would be introducing a new question—whether or not toasting the bread has any impact on how it falls. Since that’s not part of our test, we’ll stick with determining whether the presence of butter has any impact on which side hits the ground first.

#5: Analyze Data

After our experiment, we discover that both buttered toast and unbuttered toast have a 50/50 chance of hitting the ground on the buttered or marked side when dropped from a consistent height, straight down. It looks like our hypothesis was incorrect—it’s not the butter that makes the toast hit the ground in a particular way, so it must be something else.

Since we didn’t get the desired result, it’s back to the drawing board. Our hypothesis wasn’t correct, so we’ll need to start fresh. Now that you think about it, your toast seems to hit the ground butter-first when it slides off your plate, not when you drop it from a consistent height. That can be the basis for your new experiment.

#6: Communicate Your Results

Good science needs verification. Your experiment should be replicable by other people, so you can put together a report about how you ran your experiment to see if other peoples’ findings are consistent with yours.

This may be useful for class or a science fair. Professional scientists may publish their findings in scientific journals, where other scientists can read and attempt their own versions of the same experiments. Being part of a scientific community helps your experiments be stronger because other people can see if there are flaws in your approach—such as if you tested with different kinds of bread, or sometimes used peanut butter instead of butter—that can lead you closer to a good answer.

A Scientific Method Example: Falling Toast

We’ve run through a quick recap of the scientific method steps, but let’s look a little deeper by trying again to figure out why toast so often falls butter side down.

#1: Make Observations

At the end of our last experiment, where we learned that butter doesn’t actually make toast more likely to hit the ground on that side, we remembered that the times when our toast hits the ground butter side first are usually when it’s falling off a plate.

The easiest question we can ask is, “Why is that?”

We can actually search this online and find a pretty detailed answer as to why this is true. But we’re budding scientists—we want to see it in action and verify it for ourselves! After all, good science should be replicable, and we have all the tools we need to test out what’s really going on.

Why do we think that buttered toast hits the ground butter-first? We know it’s not because it’s heavier, so we can strike that out. Maybe it’s because of the shape of our plate?

That’s something we can test. We’ll phrase our hypothesis as, “If my toast slides off my plate, then it will fall butter-side down.”

Just seeing that toast falls off a plate butter-side down isn’t enough for us. We want to know why, so we’re going to take things a step further—we’ll set up a slow-motion camera to capture what happens as the toast slides off the plate.

We’ll run the test ten times, each time tilting the same plate until the toast slides off. We’ll make note of each time the butter side lands first and see what’s happening on the video so we can see what’s going on.

When we review the footage, we’ll likely notice that the bread starts to flip when it slides off the edge, changing how it falls in a way that didn’t happen when we dropped it ourselves.

That answers our question, but it’s not the complete picture —how do other plates affect how often toast hits the ground butter-first? What if the toast is already butter-side down when it falls? These are things we can test in further experiments with new hypotheses!

Now that we have results, we can share them with others who can verify our results. As mentioned above, being part of the scientific community can lead to better results. If your results were wildly different from the established thinking about buttered toast, that might be cause for reevaluation. If they’re the same, they might lead others to make new discoveries about buttered toast. At the very least, you have a cool experiment you can share with your friends!

Key Scientific Method Tips

Though science can be complex, the benefit of the scientific method is that it gives you an easy-to-follow means of thinking about why and how things happen. To use it effectively, keep these things in mind!

Don’t Worry About Proving Your Hypothesis

One of the important things to remember about the scientific method is that it’s not necessarily meant to prove your hypothesis right. It’s great if you do manage to guess the reason for something right the first time, but the ultimate goal of an experiment is to find the true reason for your observation to occur, not to prove your hypothesis right.

Good science sometimes means that you’re wrong. That’s not a bad thing—a well-designed experiment with an unanticipated result can be just as revealing, if not more, than an experiment that confirms your hypothesis.

Be Prepared to Try Again

If the data from your experiment doesn’t match your hypothesis, that’s not a bad thing. You’ve eliminated one possible explanation, which brings you one step closer to discovering the truth.

The scientific method isn’t something you’re meant to do exactly once to prove a point. It’s meant to be repeated and adapted to bring you closer to a solution. Even if you can demonstrate truth in your hypothesis, a good scientist will run an experiment again to be sure that the results are replicable. You can even tweak a successful hypothesis to test another factor, such as if we redid our buttered toast experiment to find out whether different kinds of plates affect whether or not the toast falls butter-first. The more we test our hypothesis, the stronger it becomes!

What’s Next?

Want to learn more about the scientific method? These important high school science classes will no doubt cover it in a variety of different contexts.

Test your ability to follow the scientific method using these at-home science experiments for kids !

Need some proof that science is fun? Try making slime

Melissa Brinks graduated from the University of Washington in 2014 with a Bachelor's in English with a creative writing emphasis. She has spent several years tutoring K-12 students in many subjects, including in SAT prep, to help them prepare for their college education.

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1.1: The Scientific Method

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• Susan Burran and David DesRochers
• Dalton State College via GALILEO Open Learning Materials

Adapted from http://www.biologycorner.com/

Introduction:

The scientific method is central to the study of biology: it is a process of acquiring and verifying information through experimentation. The general steps of the scientific method are depicted in the figure below. The hypothesis , or suggested explanation for the observation, is the basis for setting up experiments. A good experimental design is essential to the scientific method. A few keys to good experimental design include effective use of controls, reproducibility, a large sample size, and multiple trials.

In an experiment, in order to determine that any changes that occur are due to investigator manipulation only, there must be some basis for comparison. A control group is necessary to establish this basis of comparison. In the control group, everything is kept the same as the experimental group except for the independent variable .

The experimental group is actually being experimented upon. For example, in a drug trial, there will be a group that receives the drug (the experimental group) and a group that receives a placebo (the control group). The drug itself is considered the independent variable and any change(s) that occur because of the drug are considered the dependent variable .

In order to ensure that it is only the drug causing changes, all other variables must be tightly controlled (such as diet, exercise, smoking, etc.). These are referred to as controlled variables .

Part 1: The Strange Case of BeriBeri:

In 1887 a strange nerve disease attacked the people in the Dutch East Indies. The disease was beriberi. Symptoms of the disease included weakness and loss of appetite, victims often died of heart failure. Scientists thought the disease might be caused by bacteria. They injected chickens with bacteria from the blood of patients with beriberi. The injected chickens became sick. However, so did a group of chickens that were not injected with bacteria. One of the scientists, Dr. Eijkman, noticed something. Before the experiment, all the chickens had eaten whole-grain rice, but during the experiment, the chickens were fed polished rice. Dr. Eijkman researched this interesting case and found that polished rice lacked thiamine, a vitamin necessary for good health.

1. State the problem.

2. What was the hypothesis?

3. How was the hypothesis tested?

4. Do the results indicate that the hypothesis should be rejected?

5. What should be the new hypothesis and how would you test it?

Part 2: How Penicillin Was Discovered:

In 1928, Sir Alexander Fleming was studying Staphylococcus bacteria growing in culture dishes. He noticed that a mold called Penicillium was also growing in some of the dishes. A clear area existed around the mold because all the bacteria that had grown in this area had died. In the culture dishes without the mold, no clear areas were present. Fleming hypothesized that the mold must be producing a chemical that killed the bacteria. He decided to isolate this substance and test it to see if it would kill bacteria. Fleming transferred the mold to a nutrient broth solution. This solution contained all the materials the mold needed to grow. After the mold grew, he removed it from the nutrient broth. Fleming then added the nutrient broth in which the mold had grown to a culture of bacteria. He observed that the bacteria died which was later used to develop antibiotics used to treat a variety of diseases.

1. Identify the problem.

2. What was Fleming's hypothesis?

5. This experiment led to the development of what major medical advancement…?

Part 3: Identify the Controls and Variables

Smithers thinks that a special juice will increase the productivity of workers. He creates two groups of 50 workers each and assigns each group the same task (in this case, they're supposed to staple a set of papers). Group A is given the special juice to drink while they work. Group B is not given the special juice. After an hour, Smithers counts how many stacks of papers each group has made. Group A made 1,587 stacks; Group B made 2,113 stacks.

Identify the:

• Control Group:
• Independent Variable:
• Dependent Variable:

What should Smithers' conclusion be?

How could this experiment be improved?

Homer notices that his shower is covered in a strange green slime. His friend Barney tells him that coconut juice will get rid of the green slime. Homer decides to check this out by spraying half of the shower with coconut juice. He sprays the other half of the shower with water. After 3 days of "treatment", there is no change in the appearance of the green slime on either side of the shower.

What was the initial observation?

What should Homer's conclusion be?

Bart believes that mice exposed to radio waves will become extra strong (maybe he's been reading too much Radioactive Man). He decides to perform this experiment by placing 10 mice near a radio for 5 hours. He compared these 10 mice to another 10 mice that had not been exposed. His test consisted of a heavy block of wood that blocked the mouse food. He found that 8 out of 10 of the exposed mice were able to push the block away, while 7 out of 10 of the other mice were able to do the same.

What should Bart's conclusion be?

How could Bart's experiment be improved?

Krusty was told that a certain itching powder was the newest best thing on the market: it even claims to cause 50% longer lasting itches. Interested in this product, he buys the itching powder and compares it to his usual product. One test subject (A) is sprinkled with the original itching powder, and another test subject (B) was sprinkled with the Experimental itching powder. Subject A reported having itches for 30 minutes. Subject B reported having itches for 45 minutes

Explain whether the data supports the advertisement's claims about its product.

Design Lisa's experiment.

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• v.5(1); Spring 2006

WWW: The Scientific Method

Introduction.

Each quarter, CBE—Life Sciences Education calls attention to several Web sites of educational interest to the life science community. The journal does not endorse or guarantee the accuracy of the information at any of the listed sites. If you want to comment on the selections or suggest future inclusions, please send a message to ude.ytinirt@notsylbr . The sites listed below were last accessed on 1 December 2005.

The topic selection of the scientific method for this quarter's column was prompted in part by the recent revision of the K–12 science education standards by the Kansas State Board of Education on November 8, 2005 ( Figure 1 ). http://www.ksbe.state.ks.us/Welcome.html

Kansas State Board of Education.

Many have interpreted the November actions of the Kansas State Board of Education as allowing the teaching of “intelligent design” as an alternative to biological evolution. One may download the science standards advocated by the Board from the aforementioned Web site. A portion of their rationale for change is presented below.

Regarding the scientific theory of biological evolution, the curriculum standards call for students to learn about the best evidence for modern evolutionary theory, but also to learn about areas where scientists are raising scientific criticisms of the theory. These curriculum standards reflect the Board's objective of: 1) to help students understand the full range of scientific views that exist on this topic, 2) to enhance critical thinking and the understanding of the scientific method by encouraging students to study different and opposing scientific evidence, and 3) to ensure that science education in our state is `secular, neutral, and nonideological.'

As the debate about the actions of the Kansas State Board of Education continues, the role of the scientific method in the process of science requires clarification for many.

The scientific method is the principal methodology by which biological knowledge is gained and disseminated. As fundamental as the scientific method may be, its historical development is poorly understood, its definition is variable, and its deployment is uneven. Scientific progress may occur without the strictures imposed by the formal application of the scientific method. This report explores Web resources that get at the definition, history, and use of the scientific method.

A good place to begin this odyssey is with the organization known as Science Service. Science Service, a Washington, DC-based nonprofit organization, is best known as the publisher of Science News and as the organizer of the International Science and Engineering Fair. In its promotion of high school science, Science Service provides a Web page describing the scientific method ( Figure 2 ). http://www.sciserv.org/isef/primer/scientific_method.asp

Science Service.

One may find a carefully worded description of the scientific method consisting of the following steps: problem/purpose, hypothesis, procedure, materials, observation/data/results, analysis, and conclusion. Most would agree that this recounting of the scientific method would be appropriate for a budding young scientist, especially one who is preparing a science fair project.

Another organization that promotes science education for the K–12 audience is eMINTS (enhancing Missouri's Instructional Networked Teaching Strategies; Figure 3 ). http://www.emints.org/about/index.shtml#educators

This organization developed by three Missouri agencies (University of Missouri, the Missouri Department of Elementary and Secondary Education, and the Missouri Department of Higher Education) advocates the following: “eMINTs changes how teachers teach and students learn. Its instructional model provides a research-based approach to organizing instruction and can be implemented in any subject area at any level.” eMINTS provides a page dealing with the scientific method. http://www.emints.org/ethemes/resources/S00000408.shtml

This eMINTS Scientific Method Web page offers links to very high-quality and traditional material that includes activities such as “Does Soap Float?” and the Scientific Method scramble. One of the links available is to the Discovery School maintained by Discovery Communications ( Figure 4 ). http://school.discovery.com/sciencefaircentral/scifairstudio/handbook/scientificmethod.html

Science Fair Central.

Science Fair Central provides a five-step explanation for the scientific method: research, problem, hypothesis, project experimentation, and project conclusion. The material is derived from Janice VanCleave's Guide to the Best Science Fair Projects , a John Wiley & Sons (New York) publication.

Each of the three Web sites listed above provides a traditional and generally accepted view of the scientific method, as it would be found in support of classroom activities. Most people agree that to understand science, one must do science. The argument continues that to do science, one must use the scientific method as though it were a form of catechism with heavy emphasis on the steps used by the scientific method. For an example of placing emphasis on the steps to the method, please visit the following Web site ( Figure 5 ): http://teacher.nsrl.rochester.edu/phy_labs/AppendixE/AppendixE.html

Frank Wolfs' introduction to the scientific method.

Dr. Frank Wolfs in the Department of Physics and Astronomy at the University of Rochester (Rochester, NY) provides a scientific method appendix to the laboratory manuals associated with the introductory college physics courses at Rochester. He, as do many of his science colleagues, states that the scientific method has four steps: 1) observation and description of a phenomenon or group of phenomena; 2) formulation of a hypothesis to explain the phenomena (in physics, the hypothesis often takes the form of a causal mechanism or a mathematical relation); 3) use of the hypothesis to predict the existence of other phenomena or to predict quantitatively the results of new observations; and 4) performance of experimental tests of the predictions by several independent experimenters and properly performed experiments.

The laboratory manual for my embryology or histology course could have a similar type of statement. As we lead our students into the forest of doing science, we codify the process as requiring prescribed steps, and, like bread crumbs, these steps are to be followed through the forest. This teaching practice causes people to view science as formulaic and perhaps less of a creative process than it really is. This tendency to make the process pedantic is exemplified by the information at the following Web site ( Figure 6 ): http://www.ldolphin.org/SciMeth2.html

Lambert Dolphin's steps in the scientific method.

Lambert Dolphin of Palo Alto, CA, lays out the scientific method in a flowchart manner. Dolphin also mixes this depiction of scientific methodology with a discussion of personal philosophy and religion.

Another example of the scientific method being incorporated into a personal philosophy is associated with the following Web site ( Figure 7 ). http://www.scientificmethod.com/index.html

Norman W. Edmund's idea of the scientific method.

Norman W. Edmund is the founder of the well-known Edmund Scientific (Tonawanda, NY), a mail-order company for science supplies. His company has been sold and incorporated into a new company called Scientifics. Edmund considers the scientific method “the greatest idea of all times.” He defines the scientific method as follows: “The term `the scientific method' represents the general pattern of the types of mental activity stages (usually aided by physical activities) that occur in the master method, which we use to obtain, refine, extend and apply knowledge in all fields.”

The Science Service and eMINTS' use of the term scientific method would be generally accepted in science education fields. Dolphin and Edmund's use would be problematic for many. And in common practice as represented by the physics laboratory manuals, the scientific method is presented as a rigid process that is followed as though it were a religious doctrine. These practices lead us back to the Kansas Board of Education: “secular, neutral, and nonideological.” At this juncture, it is time to visit Charles Darwin.

Dr. Ian C. Johnston of the Department of Liberal Studies at Malaspina University-College (Nanaimo, British Columbia, Canada) has prepared a handbook for liberal arts students exploring the history of science. He gives his interpretations into the origins of evolutionary theory and in doing so provides insights into the scientific method ( Figure 8 ). http://www.mala.bc.ca/%7Ejohnstoi/darwin/sect3.htm

Ian C. Johnston's interpretations of the origins of evolutionary theory.

Darwin's delay in publishing his theory involved factors other than the stormy political climate. For what he was proposing marked a significant departure from conventional English empirical science. At the heart of natural philosophy in England, as we have seen earlier, was an emphasis on observation and experiment. Even though most scientists did not follow precisely the Baconian emphasis on the primary role of empirical observation, nevertheless, they recognized the crucial importance of experimental testing of particular hypotheses. This requirement presented Darwin with a grave methodological problem, simply because he was proposing a theory in which direct observation and experiment were clearly impossible, at least in the sense that a biologist could confirm the hypothesis of natural selection by observing it in the action of significantly transforming one species into another. Obviously, the time spans involved and the often minute succession of variations by which one species developed out of a species with quite a different appearance (e.g., reptiles from fish) meant that no direct testing by observation and experiment was possible. To meet this difficulty, Darwin developed a new scientific procedure, now known as the hypothetico-deductive method. He first developed a theory, relying upon analogy and deduction to organize a plausible explanation, without direct empirical evidence, and then applied that theory to a wide range of facts, to demonstrate the explanatory power of what he was proposing.

Johnston reminds us that the scientific method has evolved over a period of time and that the lengthy gap between Darwin's Beagle trip and the publication of the Origin of Species had to do with the limitations in the methodology of doing science at that time. Finding both irony and humor in Darwin contributing to the evolution of the scientific method, we turned to Google ( htpp://www.google.com ) to search for a history of the scientific method.

Michael James has provided an interesting essay on the history of the scientific method; the essay is a frequent hit on many search engines ( Figure 9 ). http://www.scientificmethod.co.uk/

Michael James' essay on the history of the scientific method.

James is a graduate student in the human geography department at the Open University in England. He concludes his essay with the following thought: “For every individual, science acquires systematic knowledge of the truth and laws of natural or physical phenomena that govern the world. Science classifies by definite rules. To be `scientific' is to agree with, and be well instructed in the principles of science. The manner of proceeding to an end, by orderly means, is `method'. The appearance that the use of scientific method is simply logical can be misleading, there is no more complex question of how we arrive at our thoughts.” It seems James would argue that the flow chart showing the scientific method does not cover the thinking involved in the process.

The now ubiquitous Wikipedia, the Internet encyclopedia, provides a number of portals into the history of the scientific method ( Figure 10 ). http://en.wikipedia.org/wiki/Scientific_method http://en.wikipedia.org/wiki/Baconian_method http://en.wikipedia.org/wiki/Hypothetico-deductive_method

Wikipedia's entry on Karl Popper.

Francis Bacon, a contemporary of Shakespeare, developed a method of scientific reasoning and investigation that was widely adhered to for several centuries. Johnston (above) alludes to Darwin having to deal with the Baconian method. Karl Popper developed the hypothetico-deductive method in the twentieth century and its practice involves falsification of the hypothesis. It is the falsification idea that contributes greatly to today's misunderstanding of what science is, and how the modern version of the scientific method is used. The issue of falsification is also where the Kansas Board of Education enters Dante's Divine Comedy and descends into the inferno. The Board's objective one is “to help students understand the full range of scientific views that exist on this topic.”

How many science teachers or scientists know of the Vienna Circle of science philosophers of the 1920s? These individuals developed a view of analytical philosophy including logical positivism. Karl Popper led the revolt against logical positivism set forth by the Vienna Circle. How many understand the idea of confirmation holism where a falsification of hypothesis can be undone? Who among the proponents and detractors of evolutionary theory have read Lakatos and Feyerabend's modification of Popperian ideas? The Kansas Board of Education wants “to enhance critical thinking and the understanding of the scientific method.” A place to start is at the intersection of the philosophy of science and the scientific method, and Wikipedia would make a fine first step ( Figure 11 ). http://en.wikipedia.org/wiki/Portal:Scientific_method

A comparison of Popper's, Kuhn's, and Feyerabend's ideas about scientific theories.

The scientific method has evolved. The scientific method also has critics. One place that records criticism is the Web site known as the Science Hobbyist . William J. Beaty, an electrical engineer in the Department of Chemistry at the University of Washington (Seattle, WA) hosts this site. He has a page on the site that is titled “Ten Myths of Science: Reexamining What We Think We Know...” ( Figure 12 ). http://www.amasci.com/miscon/myths10.html

Ten myths of science: reexamining what we think we know.

McComas provides an argument that “A General and Universal Scientific Method Exists” is a myth.

The notion that a common series of steps is followed by all research scientists must be among the most pervasive myths of science given the appearance of such a list in the introductory chapters of many precollege science texts. This myth has been part of the folklore of school science ever since its proposal by statistician Karl Pearson (1937). The steps listed for the scientific method vary from text to text but usually include, a) define the problem, b) gather background information, c) form a hypothesis, d) make observations, e) test the hypothesis, and f) draw conclusions. Some texts conclude their list of the steps of the scientific method by listing communication of results as the final ingredient. One of the reasons for the widespread belief in a general scientific method may be the way in which results are presented for publication in research journals. The standardized style makes it seem that scientists follow a standard research plan. Medawar (1990) reacted to the common style exhibited by research papers by calling the scientific paper a fraud since the final journal report rarely outlines the actual way in which the problem was investigated. Philosophers of science who have studied working scientists have shown that no research method is applied universally (Carey, 1994; Gibbs & Lawson, 1992; Chalmers, 1990; Gjertsen, 1989). The notion of a single scientific method is so pervasive it seems certain that many students must be disappointed when they discover that scientists do not have a framed copy of the steps of the scientific method posted high above each laboratory workbench. Close inspection will reveal that scientists approach and solve problems with imagination, creativity, prior knowledge and perseverance. These, of course, are the same methods used by all problem-solvers. The lesson to be learned is that science is no different from other human endeavors when puzzles are investigated.

An unusual place to find a discourse on the scientific method is Dharma-Haven, a site that deals with Tibetan medicine and western science. Dr. Terry Halwes of New Haven, CT, operates the site, and he posts a variety of interesting essays. One of them deals with the myth of the scientific method ( Figure 13 ). http://dharma-haven.org/science/myth-of-scientificmethod.htm

The myth of the scientific method.

Halwes argues the following: “The procedure that gets taught as `The Scientific Method' is entirely misleading. Studying what scientists actually do is far more interesting. “The site is extensive and rambling at times; however, it does pose interesting observations.

There is no such unique standard method—scientific progress requires many methods—but students in introductory science courses are taught that `The Scientific Method' is a straightforward procedure, involving testing hypotheses derived from theories in order to test those theories. The `hypothetico-deductive' schema taught to students was not developed as a method at all: It was intended to be a logical analysis of how scientific theories derive support from evidence, and it was developed in a process that intentionally excluded consideration of the process of discovery in science.

Another critique of the scientific method may be found at the University of New South Wales. Dr. John A. Schuster of the Department of History and Philosophy of Science provides a Web resource titled The Scientific Revolution: An Introduction to the History and Philosophy of Science ( Figure 14 ). http://hps.arts.unsw.edu.au/hps_content/online_resources/online_inhouse_res/schuster_SciRev_book/Schuster_a_contents.htm

The scientific revolution: an introduction to the history and philosophy of science.

Schuster's Chapter 9 is delightful and needs to be read in its entirety. The following two excerpts give the flavor of his arguments:

Method is a great story which has a wonderful history of at least 2500 years back to Aristotle, who invented the commonly accepted method story. In the 17th century we have people like Francis Bacon, Galileo, Newton who updated and approved that story. The story of method has a real function in science which unfortunately is not to tell us how science is done. In fact, its job is to mislead us as to how science is done. Method operates like a cultural myth, protecting science and scientists because it allows them to say to nonscientists why they (scientists) are special and why they should be left alone. The myth states that there is a way of doing things in science which people outside of science do not know or cannot properly use.... And so, in this century, even though this story has been criticized, there are philosophers and other people who still want to tell us that the scientific method exists. They believe a different version of the scientific method can be designed that is viable, one that at long last is the correct version. In other words, people like me are proved wrong if a good version of method becomes finally available. In the 20th century, a new 20th century method story has emerged. Its author, Sir Karl Popper, the most important philosopher of science of this century, meant to elude and reject everything we have just talked about. Many educated people believe that he succeeded, and that a Popperian version of method works and has actually been the real method of science in all times and places. We shall now see what that new method story involves, what are its undoubted strengths, and why in the end, we probably must conclude that it, like all previous method tales, from Aristotle to Newton, functions only as myth and rhetorical packaging.

All of the above leads to the third objective of the Kansas Board of Education: “to ensure that science education in our state is `secular, neutral, and nonideological.'” Is the process of science about making choices? This experiment is correct. This experiment is wrong. This conclusion is correct. This conclusion is wrong. Based on these choices, science moves forward. If science education is to be “neutral,” then one cannot make choices. One cannot act on the results of tested hypotheses. To act means that one can no longer be neutral. A definition of ideology is “the ideas and manner of thinking characteristic of a group, social class, or individual.” To have no ideology suggests that the group has no ideas or manner of thinking. One might assume the Kansas Board of Education would like science to have no manner of thinking, no scientific method as it were, for to have a method is an expression of ideology. If scientists agree on a particular natural phenomenon, is it ideological to agree with this “theory” and act upon it?

Focusing on the scientific method, is it prescriptive or descriptive? Of course, the answer is yes and no. It describes a process by which science can be done. And yet, many valid experiments in science today are not hypothesis driven. If an experiment is not hypothesis driven, is it following the scientific method? Can science be performed only through steps associated with the scientific method? In a sense then, the method is being treated as prescriptive. If science is the baby, is the scientific method the bathwater? If we throw out the bathwater, do we run too great a risk of losing the baby?

The scientific method is a convenient way to introduce students to the process of science. It is an approximation. As the student matures, how we teach what constitutes the scientific method should mature as well to include less black-and-white and more gray. We trust this brief review of Web resources on the topic of the scientific method will help your students gain a better understanding of the process of science and its relationship to its philosophy.

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Scientific Discovery

Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences. Philosophical discussions of scientific discovery have been intricate and complex because the term “discovery” has been used in many different ways, both to refer to the outcome and to the procedure of inquiry. In the narrowest sense, the term “discovery” refers to the purported “eureka moment” of having a new insight. In the broadest sense, “discovery” is a synonym for “successful scientific endeavor” tout court. Some philosophical disputes about the nature of scientific discovery reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the nature of human creativity, specifically about whether the “eureka moment” can be analyzed and about whether there are rules (algorithms, guidelines, or heuristics) according to which such a novel insight can be brought about. Philosophical issues also arise about the analysis and evaluation of heuristics, about the characteristics of hypotheses worthy of articulation and testing, and, on the meta-level, about the nature and scope of philosophical analysis itself. This essay describes the emergence and development of the philosophical problem of scientific discovery and surveys different philosophical approaches to understanding scientific discovery. In doing so, it also illuminates the meta-philosophical problems surrounding the debates, and, incidentally, the changing nature of philosophy of science.

1. Introduction

2. scientific inquiry as discovery, 3. elements of discovery, 4. pragmatic logics of discovery, 5. the distinction between the context of discovery and the context of justification, 6.1 discovery as abduction, 6.2 heuristic programming, 7. anomalies and the structure of discovery, 8.1 discoverability, 8.2 preliminary appraisal, 8.3 heuristic strategies, 9.1 kinds and features of creativity, 9.2 analogy, 9.3 mental models, 10. machine discovery, 11. social epistemology and discovery, 12. integrated approaches to knowledge generation, other internet resources, related entries.

Philosophical reflection on scientific discovery occurred in different phases. Prior to the 1930s, philosophers were mostly concerned with discoveries in the broad sense of the term, that is, with the analysis of successful scientific inquiry as a whole. Philosophical discussions focused on the question of whether there were any discernible patterns in the production of new knowledge. Because the concept of discovery did not have a specified meaning and was used in a very wide sense, almost all discussions of scientific method and practice could potentially be considered as early contributions to reflections on scientific discovery. In the course of the 18 th century, as philosophy of science and science gradually became two distinct endeavors with different audiences, the term “discovery” became a technical term in philosophical discussions. Different elements of scientific inquiry were specified. Most importantly, during the 19 th century, the generation of new knowledge came to be clearly and explicitly distinguished from its assessment, and thus the conditions for the narrower notion of discovery as the act or process of conceiving new ideas emerged. This distinction was encapsulated in the so-called “context distinction,” between the “context of discovery” and the “context of justification”.

Much of the discussion about scientific discovery in the 20 th century revolved around this distinction It was argued that conceiving a new idea is a non-rational process, a leap of insight that cannot be captured in specific instructions. Justification, by contrast, is a systematic process of applying evaluative criteria to knowledge claims. Advocates of the context distinction argued that philosophy of science is exclusively concerned with the context of justification. The assumption underlying this argument is that philosophy is a normative project; it determines norms for scientific practice. Given this assumption, only the justification of ideas, not their generation, can be the subject of philosophical (normative) analysis. Discovery, by contrast, can only be a topic for empirical study. By definition, the study of discovery is outside the scope of philosophy of science proper.

The introduction of the context distinction and the disciplinary distinction between empirical science studies and normative philosophy of science that was tied to it spawned meta-philosophical disputes. For a long time, philosophical debates about discovery were shaped by the notion that philosophical and empirical analyses are mutually exclusive. Some philosophers insisted, like their predecessors prior to the 1930s, that the philosopher’s tasks include the analysis of actual scientific practices and that scientific resources be used to address philosophical problems. They maintained that it is a legitimate task for philosophy of science to develop a theory of heuristics or problem solving. But this position was the minority view in philosophy of science until the last decades of the 20 th century. Philosophers of discovery were thus compelled to demonstrate that scientific discovery was in fact a legitimate part of philosophy of science. Philosophical reflections about the nature of scientific discovery had to be bolstered by meta-philosophical arguments about the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical research are not mutually exclusive. Not only do empirical studies of actual scientific discoveries in past and present inform philosophical thought about the structure and cognitive mechanisms of discovery, but works in psychology, cognitive science, artificial intelligence and related fields have become integral parts of philosophical analyses of the processes and conditions of the generation of new knowledge. Social epistemology has opened up another perspective on scientific discovery, reconceptualizing knowledge generation as group process.

Prior to the 19 th century, the term “discovery” was used broadly to refer to a new finding, such as a new cure, an unknown territory, an improvement of an instrument, or a new method of measuring longitude. One strand of the discussion about discovery dating back to ancient times concerns the method of analysis as the method of discovery in mathematics and geometry, and, by extension, in philosophy and scientific inquiry. Following the analytic method, we seek to find or discover something – the “thing sought,” which could be a theorem, a solution to a geometrical problem, or a cause – by analyzing it. In the ancient Greek context, analytic methods in mathematics, geometry, and philosophy were not clearly separated; the notion of finding or discovering things by analysis was relevant in all these fields.

In the ensuing centuries, several natural and experimental philosophers, including Avicenna and Zabarella, Bacon and Boyle, the authors of the Port-Royal Logic and Newton, and many others, expounded rules of reasoning and methods for arriving at new knowledge. The ancient notion of analysis still informed these rules and methods. Newton’s famous thirty-first query in the second edition of the Opticks outlines the role of analysis in discovery as follows: “As in Mathematicks, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions, but such as are taken from Experiments, or other certain Truths … By this way of Analysis we may proceed from Compounds to Ingredients, and from Motions to the Forces producing them; and in general, from Effects to their Causes, and from particular Causes to more general ones, till the Argument end in the most general. This is the Method of Analysis” (Newton 1718, 380, see Koertge 1980, section VI). Early modern accounts of discovery captured knowledge-seeking practices in the study of living and non-living nature, ranging from astronomy and physics to medicine, chemistry, and agriculture. These rich accounts of scientific inquiry were often expounded to bolster particular theories about the nature of matter and natural forces and were not explicitly labeled “methods of discovery ”, yet they are, in fact, accounts of knowledge generation and proper scientific reasoning, covering topics such as the role of the senses in knowledge generation, observation and experimentation, analysis and synthesis, induction and deduction, hypotheses, probability, and certainty.

Bacon’s work is a prominent example. His view of the method of science as it is presented in the Novum Organum showed how best to arrive at knowledge about “form natures” (the most general properties of matter) via a systematic investigation of phenomenal natures. Bacon described how first to collect and organize natural phenomena and experimentally produced facts in tables, how to evaluate these lists, and how to refine the initial results with the help of further trials. Through these steps, the investigator would arrive at conclusions about the “form nature” that produces particular phenomenal natures. Bacon expounded the procedures of constructing and evaluating tables of presences and absences to underpin his matter theory. In addition, in his other writings, such as his natural history Sylva Sylvarum or his comprehensive work on human learning De Augmentis Scientiarium , Bacon exemplified the “art of discovery” with practical examples and discussions of strategies of inquiry.

Like Bacon and Newton, several other early modern authors advanced ideas about how to generate and secure empirical knowledge, what difficulties may arise in scientific inquiry, and how they could be overcome. The close connection between theories about matter and force and scientific methodologies that we find in early modern works was gradually severed. 18 th - and early 19 th -century authors on scientific method and logic cited early modern approaches mostly to model proper scientific practice and reasoning, often creatively modifying them ( section 3 ). Moreover, they developed the earlier methodologies of experimentation, observation, and reasoning into practical guidelines for discovering new phenomena and devising probable hypotheses about cause-effect relations.

It was common in 20 th -century philosophy of science to draw a sharp contrast between those early theories of scientific method and modern approaches. 20 th -century philosophers of science interpreted 17 th - and 18 th -century approaches as generative theories of scientific method. They function simultaneously as guides for acquiring new knowledge and as assessments of the knowledge thus obtained, whereby knowledge that is obtained “in the right way” is considered secure (Laudan 1980; Schaffner 1993: chapter 2). On this view, scientific methods are taken to have probative force (Nickles 1985). According to modern, “consequentialist” theories, propositions must be established by comparing their consequences with observed and experimentally produced phenomena (Laudan 1980; Nickles 1985). It was further argued that, when consequentialist theories were on the rise, the two processes of generation and assessment of an idea or hypothesis became distinct, and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

More recent research in history of philosophy of science has shown, however, that there was no such sharp contrast. Consequentialist ideas were advanced throughout the 18 th century, and the early modern generative theories of scientific method and knowledge were more pragmatic than previously assumed. Early modern scholars did not assume that this procedure would lead to absolute certainty. One could only obtain moral certainty for the propositions thus secured.

During the 18 th and 19 th centuries, the different elements of discovery gradually became separated and discussed in more detail. Discussions concerned the nature of observations and experiments, the act of having an insight and the processes of articulating, developing, and testing the novel insight. Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes.

Numerous 19 th -century scholars contributed to these discussions, including Claude Bernard, Auguste Comte, George Gore, John Herschel, W. Stanley Jevons, Justus von Liebig, John Stuart Mill, and Charles Sanders Peirce, to name only a few. William Whewell’s work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is a noteworthy and, later, much discussed contribution to the philosophical debates about scientific discovery because he explicitly distinguished the creative moment or “happy thought” as he called it from other elements of scientific inquiry and because he offered a detailed analysis of the “discoverer’s induction”, i.e., the pursuit and evaluation of the new insight. Whewell’s approach is not unique, but for late 20 th -century philosophers of science, his comprehensive, historically informed philosophy of discovery became a point of orientation in the revival of interest in scientific discovery processes.

For Whewell, discovery comprised three elements: the happy thought, the articulation and development of that thought, and the testing or verification of it. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. According to Whewell, the initial step in every discovery is what he called “some happy thought, of which we cannot trace the origin; some fortunate cast of intellect, rising above all rules. No maxims can be given which inevitably lead to discovery” (Whewell 1996 [1840]: 186). An “art of discovery” in the sense of a teachable and learnable skill does not exist according to Whewell. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a wild guess. Only the person whose mind is prepared to see things will actually notice them. The “previous condition of the intellect, and not the single fact, is really the main and peculiar cause of the success. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later. It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second element of a scientific discovery consists in binding together—“colligating”, as Whewell called it—a set of facts by bringing them under a general conception. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. Colligation involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is extended and iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. Verification also involves judging the predictive power, simplicity, and “consilience” of the outcome of the colligation. “Consilience” refers to a higher range of generality (broader applicability) of the theory (the articulated and clarified happy thought) that the actual colligation produced. Whewell’s account of discovery is not a deductivist system. It is essential that the outcome of the colligation be inferable from the data prior to any testing (Snyder 1997).

Whewell’s theory of discovery clearly separates three elements: the non-analyzable happy thought or eureka moment; the process of colligation which includes the clarification and explication of facts and ideas; and the verification of the outcome of the colligation. His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery. In contrast to many 20 th -century approaches, Whewell’s philosophical conception of discovery also comprises the processes by which the happy thoughts are articulated. Similarly, the process of verification is an integral part of discovery. The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation (Ducasse 1951).

Whewell’s conceptualization of scientific discovery offers a useful framework for mapping the philosophical debates about discovery and for identifying major issues of concern in 20 th -century philosophical debates. Until the late 20 th century, most philosophers operated with a notion of discovery that is narrower than Whewell’s. In more recent treatments of discovery, however, the scope of the term “discovery” is limited to either the first of these elements, the “happy thought”, or to the happy thought and its initial articulation. In the narrower conception, what Whewell called “verification” is not part of discovery proper. Secondly, until the late 20 th century, there was wide agreement that the eureka moment, narrowly construed, is an unanalyzable, even mysterious leap of insight. The main disagreements concerned the question of whether the process of developing a hypothesis (the “colligation” in Whewell’s terms) is, or is not, a part of discovery proper – and if it is, whether and how this process is guided by rules. Much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought. Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules.

In early 20 th -century logical empiricism, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread. Alternative conceptions of discovery especially in the pragmatist tradition emphasize that discovery is an extended process, i.e., that the discovery process includes the reasoning processes through which a new insight is articulated and further developed.

In the pragmatist tradition, the term “logic” is used in the broad sense to refer to strategies of human reasoning and inquiry. While the reasoning involved does not proceed according to the principles of demonstrative logic, it is systematic enough to deserve the label “logical”. Proponents of this view argued that traditional (here: syllogistic) logic is an inadequate model of scientific discovery because it misrepresents the process of knowledge generation as grossly as the notion of an “aha moment”.

Early 20 th -century pragmatic logics of discovery can best be described as comprehensive theories of the mental and physical-practical operations involved in knowledge generation, as theories of “how we think” (Dewey 1910). Among the mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning; among the physical operations are observation and (laboratory) experiments. These features of scientific discovery are either not or only insufficiently represented by traditional syllogistic logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions. These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative (Carmichael 1922, 1930). One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning (Schiller 1917; Benjamin 1934, see also section 9.2 .). However, in academic philosophy of science, endeavors to develop more systematically the heuristics guiding discovery processes were soon eclipsed by the advance of the distinction between contexts of discovery and justification.

The distinction between “context of discovery” and “context of justification” dominated and shaped the discussions about discovery in 20 th -century philosophy of science. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense (test, verification) of it. As the previous sections have shown, the distinction among different elements of scientific inquiry has a long history but in the first half of the 20 th century, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion between “genuine” philosophy and other fields of science studies, which became potent in philosophy of science. The boundary between context of discovery (the de facto thinking processes) and context of justification (the de jure defense of the correctness of these thoughts) was now understood to determine the scope of philosophy of science, whereby philosophy of science is conceived as a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, nonrational process; it cannot be subject to normative analysis. Therefore, the study of scientists’ actual thinking can only be the subject of psychology, sociology, and other empirical sciences. Philosophy of science, by contrast, is exclusively concerned with the context of justification.

The terms “context of discovery” and “context of justification” are often associated with Hans Reichenbach’s work. Reichenbach’s original conception of the context distinction is quite complex, however (Howard 2006; Richardson 2006). It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is. Descriptive philosophy of science reconstructs scientists’ thinking processes in such a way that logical analysis can be performed on them, and it thus prepares the ground for the evaluation of these thoughts (Reichenbach 1938: § 1). Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context of justification” are widely used, there has been ample discussion about how the distinction should be drawn and what their philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar 1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3; Schickore and Steinle 2006). Most commonly, the distinction is interpreted as a distinction between the process of conceiving a theory and the assessment of that theory, specifically the assessment of the theory’s epistemic support. This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then assessed. Rather, generation and assessment are two different epistemic approaches to theory: the endeavor to articulate, flesh out, and develop its potential and the endeavor to assess its epistemic worth. Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory. The first option is to characterize the generation of new knowledge as an irrational act, a mysterious creative intuition, a “eureka moment”. The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea.

Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery. In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. This objection to philosophies of discovery has been called the “discovery machine objection” (Curd 1980: 207). It is usually associated with Karl Popper’s Logic of Scientific Discovery .

The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge. This latter is concerned not with questions of fact (Kant’s quid facti ?) , but only with questions of justification or validity (Kant’s quid juris ?) . Its questions are of the following kind. Can a statement be justified? And if so, how? Is it testable? Is it logically dependent on certain other statements? Or does it perhaps contradict them? […]Accordingly I shall distinguish sharply between the process of conceiving a new idea, and the methods and results of examining it logically. As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained. (Popper 2002 [1934/1959]: 7–8)

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological or sociological theory. In this perspective, “discovery” is understood as a retrospective label, which is attributed as a sign of accomplishment to some scientific endeavors. Sociological theories acknowledge that discovery is a collective achievement and the outcome of a process of negotiation through which “discovery stories” are constructed and certain knowledge claims are granted discovery status (Brannigan 1981; Schaffer 1986, 1994).

The impact of the context distinction on 20 th -century studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery (however construed) is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century. The last section shows that there were some attempts to develop logics of discovery in the 1920s and 1930s, especially in the pragmatist tradition. But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science and that, therefore, discovery was not a legitimate topic for philosophy of science. Until the last decades of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only during the 1970s did the interest in philosophical approaches to discovery begin to increase again. But the context distinction remained a challenge for philosophies of discovery.

There are several lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology. All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one. Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

But the different approaches to scientific discovery employ different terminologies. In particular, the term “logic” of discovery is sometimes used in a narrow sense and sometimes broadly understood. In the narrow sense, “logic” of discovery is understood to refer to a set of formal, generally applicable rules by which novel ideas can be mechanically derived from existing data. In the broad sense, “logic” of discovery refers to the schematic representation of reasoning procedures. “Logical” is just another term for “rational”. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures. Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz. generation and assessments of new ideas. At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and assessment of knowledge. In the latter framework, the label “discovery” is sometimes used to refer just to generation and sometimes to refer to both generation and pursuit.

One response to the challenge of the context distinction draws on a broad understanding of the term “logic” to argue that we cannot but admit a general, domain-neutral logic if we do not want to assume that the success of science is a miracle (Jantzen 2016) and that a logic of scientific discovery can be developed ( section 6 ). Another response, drawing on a narrow understanding of the term “logic”, is to concede that there is no logic of discovery, i.e., no algorithm for generating new knowledge, but that the process of discovery follows an identifiable, analyzable pattern ( section 7 ).

Others argue that discovery is governed by a methodology . The methodology of discovery is a legitimate topic for philosophical analysis ( section 8 ). Yet another response assumes that discovery is or at least involves a creative act. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, philosophers have sought to demystify the cognitive processes involved in the generation of new ideas. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis ( section 9.1 ).

All these responses assume that there is more to discovery than a eureka moment. Discovery comprises processes of articulating, developing, and assessing the creative thought, as well as the scientific community’s adjudication of what does, and does not, count as “discovery” (Arabatzis 1996). These are the processes that can be examined with the tools of philosophical analysis, augmented by input from other fields of science studies such as sociology, history, or cognitive science.

6. Logics of discovery after the context distinction

One way of responding to the demarcation criterion described above is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis. They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning ( section 6.1 ). The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems ( section 6.2 ). Both lines of argument rely on a broad conception of logic, whereby the “logic” of discovery amounts to a schematic account of the reasoning processes involved in knowledge generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The special logic of discovery is the logic of abductive or “retroductive” inferences (Hanson 1958). The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.S. Peirce. This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena.

In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The scientists’ reasoning proceeds abductively from an anomaly to an explanatory hypothesis in light of which the phenomena would no longer be surprising or anomalous. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention (Hanson 1965: 64). According to Hanson, the abductive argument has the following schematic form (Hanson 1960: 104):

• Some surprising, astonishing phenomena p 1 , p 2 , p 3 … are encountered.
• But p 1 , p 2 , p 3 … would not be surprising were an hypothesis of H ’s type to obtain. They would follow as a matter of course from something like H and would be explained by it.
• Therefore there is good reason for elaborating an hypothesis of type H—for proposing it as a possible hypothesis from whose assumption p 1 , p 2 , p 3 … might be explained.

Drawing on the historical record, Hanson argues that several important discoveries were made relying on abductive reasoning, such as Kepler’s discovery of the elliptic orbit of Mars (Hanson 1958). It is now widely agreed, however, that Hanson’s reconstruction of the episode is not a historically adequate account of Kepler’s discovery (Lugg 1985). More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences. Even if one accepts Hanson’s schematic representation of the process of identifying plausible hypotheses, this process is a “logical” process only in the widest sense whereby the term “logical” is understood as synonymous with “rational”. Notably, some philosophers have even questioned the rationality of abductive inferences (Koehler 1991; Brem and Rips 2000).

Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis (Harman 1965; see also Blackwell 1969). Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type. The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise (Laudan 1980; Schaffner 1993).

In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: the common notion of abduction as inference to the best explanation (selective abduction) and creative abduction (Magnani 2000, 2009). Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses. Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain (selective abduction) to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce’s original account of abduction and to include not only verbal information but also non-verbal mental representations, such as visual, auditory, or motor representations. In Thagard’s approach, representations are characterized as patterns of activity in mental populations (see also section 9.3 below). The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. Surprise, for instance, could be characterized as resulting from rapid changes in activation of the node in a neural network representing the “surprising” element (Thagard and Stewart 2011). If all mental representations can be characterized as patterns of firing in neural populations, abduction can be analyzed as the combination or “convolution” (Thagard) of patterns of neural activity from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science. In this approach, scientific discovery is treated as a form of problem-solving activity (Simon 1973; see also Newell and Simon 1971), whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search (e.g., Langley et al. 1987). In computational heuristics, search programs can be described as searches for solutions in a so-called “problem space” in a certain domain. The problem space comprises all possible configurations in that domain (e.g., for chess problems, all possible arrangements of pieces on a board of chess). Each configuration is a “state” of the problem space. There are two special states, namely the goal state, i.e., the state to be reached, and the initial state, i.e., the configuration at the starting point from which the search begins. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached (Langley et al. 1987: chapter 9). A problem solution is a sequence of operations leading from the initial to the goal state.

The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space. These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. The term “heuristic search” indicates that in contrast to algorithms, problem-solving procedures lead to results that are merely provisional and plausible. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. In this context, the term “logic” is not used in the narrow sense of a set of formal, generally applicable rules to draw inferences but again in a broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. Computer programs such as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988) utilize sets of problem-solving heuristics to detect regularities in given data sets. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related. It would thus “infer” that the dependent term always has that value or that a linear relation exists between x and y . These programs can “make discoveries” in the sense that they can simulate successful discoveries such as Kepler’s third law (BACON) or the Krebs cycle (KEKADA).

Computational theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators (processes that will change the situation) that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined (Simon 1977). Computational approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, the early computational heuristics have some limitations. Compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated (Bechtel and Richardson 1993: chapter 1). Because a computer program requires the data from actual experiments, the simulations cover only certain aspects of scientific discoveries; in particular, it cannot determine by itself which data is relevant, which data to relate and what form of law it should look for (Gillies 1996). However, as a consequence of the rise of so-called “deep learning” methods in data-intensive science, there is renewed philosophical interest in the question of whether machines can make discoveries ( section 10 ).

Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. One very influential approach is Thomas Kuhn’s analysis of the emergence of novel facts and theories (Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery as part of his account of scientific change. A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms. A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: observations of an anomalous phenomenon, attempts to conceptualize it, and changes in the paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science. The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:

novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong. Anomaly appears only against the background provided by the paradigm. (Kuhn 1970 [1962]: 65)

Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery. Kuhn illustrates these points with the discovery of oxygen (see Kuhn 1970 [1962]: 53–56). Oxygen had not been discovered before 1774 and had been discovered by 1777. Even before 1774, Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. W. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. But Scheele’s work remained unpublished until after 1777, and Priestley did not identify his substance as a new sort of gas. In 1777, Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry. But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. It was an atomic “principle of acidity” and oxygen gas was a combination of that principle with caloric. According to Kuhn, all of these developments are part of the discovery of oxygen, but none of them can be singled out as “the” act of discovery.

In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected. In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. It takes time for the investigators to recognize “both that something is and what it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

Recent studies in cognitive neuroscience of brain activity during periods of conceptual change support Kuhn’s view that conceptual change is hard to achieve. These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories. The studies suggest that the two types of data are processed differently (Dunbar et al. 2007).

8. Methodologies of discovery

Advocates of the view that there are methodologies of discovery use the term “logic” in the narrow sense of an algorithmic procedure to generate new ideas. But like the AI-based theories of scientific discovery described in section 6 , methodologies of scientific discovery interpret the concept “discovery” as a label for an extended process of generating and articulating new ideas and often describe the process in terms of problem solving. In these approaches, the distinction between the contexts of discovery and the context of justification is challenged because the methodology of discovery is understood to play a justificatory role. Advocates of a methodology of discovery usually rely on a distinction between different justification procedures, justification involved in the process of generating new knowledge and justification involved in testing it. Consequential or “strong” justifications are methods of testing. The justification involved in discovery, by contrast, is conceived as generative (as opposed to consequential) justification ( section 8.1 ) or as weak (as opposed to strong) justification ( section 8.2 ). Again, some terminological ambiguity exists because according to some philosophers, there are three contexts, not two: Only the initial conception of a new idea (the creative act is the context of discovery proper, and between it and justification there exists a separate context of pursuit (Laudan 1980). But many advocates of methodologies of discovery regard the context of pursuit as an integral part of the process of justification. They retain the notion of two contexts and re-draw the boundaries between the contexts of discovery and justification as they were drawn in the early 20 th century.

The methodology of discovery has sometimes been characterized as a form of justification that is complementary to the methodology of testing (Nickles 1984, 1985, 1989). According to the methodology of testing, empirical support for a theory results from successfully testing the predictive consequences derived from that theory (and appropriate auxiliary assumptions). In light of this methodology, justification for a theory is “consequential justification,” the notion that a hypothesis is established if successful novel predictions are derived from the theory or claim. Generative justification complements consequential justification. Advocates of generative justification hold that there exists an important form of justification in science that involves reasoning to a claim from data or previously established results more generally.

One classic example for a generative methodology is the set of Newton’s rules for the study of natural philosophy. According to these rules, general propositions are established by deducing them from the phenomena. The notion of generative justification seeks to preserve the intuition behind classic conceptions of justification by deduction. Generative justification amounts to the rational reconstruction of the discovery path in order to establish its discoverability had the researchers known what is known now, regardless of how it was first thought of (Nickles 1985, 1989). The reconstruction demonstrates in hindsight that the claim could have been discovered in this manner had the necessary information and techniques been available. In other words, generative justification—justification as “discoverability” or “potential discovery”—justifies a knowledge claim by deriving it from results that are already established. While generative justification does not retrace exactly those steps of the actual discovery path that were actually taken, it is a better representation of scientists’ actual practices than consequential justification because scientists tend to construe new claims from available knowledge. Generative justification is a weaker version of the traditional ideal of justification by deduction from the phenomena. Justification by deduction from the phenomena is complete if a theory or claim is completely determined from what we already know. The demonstration of discoverability results from the successful derivation of a claim or theory from the most basic and most solidly established empirical information.

Discoverability as described in the previous paragraphs is a mode of justification. Like the testing of novel predictions derived from a hypothesis, generative justification begins when the phase of finding and articulating a hypothesis worthy of assessing is drawing to a close. Other approaches to the methodology of discovery are directly concerned with the procedures involved in devising new hypotheses. The argument in favor of this kind of methodology is that the procedures of devising new hypotheses already include elements of appraisal. These preliminary assessments have been termed “weak” evaluation procedures (Schaffner 1993). Weak evaluations are relevant during the process of devising a new hypothesis. They provide reasons for accepting a hypothesis as promising and worthy of further attention. Strong evaluations, by contrast, provide reasons for accepting a hypothesis as (approximately) true or confirmed. Both “generative” and “consequential” testing as discussed in the previous section are strong evaluation procedures. Strong evaluation procedures are rigorous and systematically organized according to the principles of hypothesis derivation or H-D testing. A methodology of preliminary appraisal, by contrast, articulates criteria for the evaluation of a hypothesis prior to rigorous derivation or testing. It aids the decision about whether to take that hypothesis seriously enough to develop it further and test it. For advocates of this version of the methodology of discovery, it is the task of philosophy of science to characterize sets of constraints and methodological rules guiding the complex process of prior-to-test evaluation of hypotheses.

In contrast to the computational approaches discussed above, strategies of preliminary appraisal are not regarded as subject-neutral but as specific to particular fields of study. Philosophers of biology, for instance, have developed a fine-grained framework to account for the generation and preliminary evaluation of biological mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993; Craver and Darden 2013). Some philosophers have suggested that the phase of preliminary appraisal be further divided into two phases, the phase of appraising and the phase of revising. According to Lindley Darden, the phases of generation, appraisal and revision of descriptions of mechanisms can be characterized as reasoning processes governed by reasoning strategies. Different reasoning strategies govern the different phases (Darden 1991, 2002; Craver 2002; Darden 2009). The generation of hypotheses about mechanisms, for instance, is governed by the strategy of “schema instantiation” (see Darden 2002). The discovery of the mechanism of protein synthesis involved the instantiation of an abstract schema for chemical reactions: reactant 1 + reactant 2 = product. The actual mechanism of protein synthesis was found through specification and modification of this schema.

Neither of these strategies is deemed necessary for discovery, and they are not prescriptions for biological research. Rather, these strategies are deemed sufficient for the discovery of mechanisms. The methodology of the discovery of mechanisms is an extrapolation from past episodes of research on mechanisms and the result of a synthesis of rational reconstructions of several of these historical episodes. The methodology of discovery is weakly normative in the sense that the strategies for the discovery of mechanisms that were successful in the past may prove useful in future biological research (Darden 2002).

As philosophers of science have again become more attuned to actual scientific practices, interest in heuristic strategies has also been revived. Many analysts now agree that discovery processes can be regarded as problem solving activities, whereby a discovery is a solution to a problem. Heuristics-based methodologies of discovery are neither purely subjective and intuitive nor algorithmic or formalizable; the point is that reasons can be given for pursuing one or the other problem-solving strategy. These rules are open and do not guarantee a solution to a problem when applied (Ippoliti 2018). On this view, scientific researchers are no longer seen as Kuhnian “puzzle solvers” but as problem solvers and decision makers in complex, variable, and changing environments (Wimsatt 2007).

Philosophers of discovery working in this tradition draw on a growing body of literature in cognitive psychology, management science, operations research, and economy on human reasoning and decision making in contexts with limited information, under time constraints, and with sub-optimal means (Gigerenzer & Sturm 2012). Heuristic strategies characterized in these studies, such as Gigerenzer’s “tools to theory heuristic” are then applied to understand scientific knowledge generation (Gigerenzer 1992, Nickles 2018). Other analysts specify heuristic strategies in a range of scientific fields, including climate science, neurobiology, and clinical medicine (Gramelsberger 2011, Schaffner 2008, Gillies 2018). Finally, in analytic epistemology, formal methods are developed to identify and assess distinct heuristic strategies currently in use, such as Bayesian reverse engineering in cognitive science (Zednik and Jäkel 2016).

As the literature on heuristics continues to grow, it has become clear that the term “heuristics” is itself used in a variety of different ways. (For a valuable taxonomy of meanings of “heuristic,” see Chow 2015, see also Ippoliti 2018.) Moreover, as in the context of earlier debates about computational heuristics, debates continue about the limitations of heuristics. The use of heuristics may come at a cost if heuristics introduce systematic biases (Wimsatt 2007). Some philosophers thus call for general principles for the evaluation of heuristic strategies (Hey 2016).

9. Cognitive perspectives on discovery

The approaches to scientific discovery presented in the previous sections focus on the adoption, articulation, and preliminary evaluation of ideas or hypotheses prior to rigorous testing, not on how a novel hypothesis or idea is first thought up. For a long time, the predominant view among philosophers of discovery was that the initial step of discovery is a mysterious intuitive leap of the human mind that cannot be analyzed further. More recent accounts of discovery informed by evolutionary biology also do not explicate how new ideas are formed. The generation of new ideas is akin to random, blind variations of thought processes, which have to be inspected by the critical mind and assessed as neutral, productive, or useless (Campbell 1960; see also Hull 1988), but the key processes by which new ideas are generated are left unanalyzed.

With the recent rapprochement among philosophy of mind, cognitive science and psychology and the increased integration of empirical research into philosophy of science, these processes have been submitted to closer analysis, and philosophical studies of creativity have seen a surge of interest (e.g. Paul & Kaufman 2014a). The distinctive feature of these studies is that they integrate philosophical analyses with empirical work from cognitive science, psychology, evolutionary biology, and computational neuroscience (Thagard 2012). Analysts have distinguished different kinds and different features of creative thinking and have examined certain features in depth, and from new angles. Recent philosophical research on creativity comprises conceptual analyses and integrated approaches based on the assumption that creativity can be analyzed and that empirical research can contribute to the analysis (Paul & Kaufman 2014b). Two key elements of the cognitive processes involved in creative thinking that have been in the focus of philosophical analysis are analogies ( section 9.2 ) and mental models ( section 9.3 ).

General definitions of creativity highlight novelty or originality and significance or value as distinctive features of a creative act or product (Sternberg & Lubart 1999, Kieran 2014, Paul & Kaufman 2014b, although see Hills & Bird 2019). Different kinds of creativity can be distinguished depending on whether the act or product is novel for a particular individual or entirely novel. Psychologist Margaret Boden distinguishes between psychological creativity (P-creativity) and historical creativity (H-creativity). P-creativity is a development that is new, surprising and important to the particular person who comes up with it. H-creativity, by contrast, is radically novel, surprising, and important—it is generated for the first time (Boden 2004). Further distinctions have been proposed, such as anthropological creativity (construed as a human condition) and metaphysical creativity, a radically new thought or action in the sense that it is unaccounted for by antecedents and available knowledge, and thus constitutes a radical break with the past (Kronfeldner 2009, drawing on Hausman 1984).

Psychological studies analyze the personality traits and creative individuals’ behavioral dispositions that are conducive to creative thinking. They suggest that creative scientists share certain distinct personality traits, including confidence, openness, dominance, independence, introversion, as well as arrogance and hostility. (For overviews of recent studies on personality traits of creative scientists, see Feist 1999, 2006: chapter 5).

Recent work on creativity in philosophy of mind and cognitive science offers substantive analyses of the cognitive and neural mechanisms involved in creative thinking (Abrams 2018, Minai et al 2022) and critical scrutiny of the romantic idea of genius creativity as something deeply mysterious (Blackburn 2014). Some of this research aims to characterize features that are common to all creative processes, such as Thagard and Stewart’s account according to which creativity results from combinations of representations (Thagard & Stewart 2011, but see Pasquale and Poirier 2016). Other research aims to identify the features that are distinctive of scientific creativity as opposed to other forms of creativity, such as artistic creativity or creative technological invention (Simonton 2014).

Many philosophers of science highlight the role of analogy in the development of new knowledge, whereby analogy is understood as a process of bringing ideas that are well understood in one domain to bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An important source for philosophical thought about analogy is Mary Hesse’s conception of models and analogies in theory construction and development. In this approach, analogies are similarities between different domains. Hesse introduces the distinction between positive, negative, and neutral analogies (Hesse 1966: 8). If we consider the relation between gas molecules and a model for gas, namely a collection of billiard balls in random motion, we will find properties that are common to both domains (positive analogy) as well as properties that can only be ascribed to the model but not to the target domain (negative analogy). There is a positive analogy between gas molecules and a collection of billiard balls because both the balls and the molecules move randomly. There is a negative analogy between the domains because billiard balls are colored, hard, and shiny but gas molecules do not have these properties. The most interesting properties are those properties of the model about which we do not know whether they are positive or negative analogies. This set of properties is the neutral analogy. These properties are the significant properties because they might lead to new insights about the less familiar domain. From our knowledge about the familiar billiard balls, we may be able to derive new predictions about the behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical reasoning through the distinction between horizontal and vertical analogies between domains. Horizontal analogies between two domains concern the sameness or similarity between properties of both domains. If we consider sound and light waves, there are similarities between them: sound echoes, light reflects; sound is loud, light is bright, both sound and light are detectable by our senses. There are also relations among the properties within one domain, such as the causal relation between sound and the loud tone we hear and, analogously, between physical light and the bright light we see. These analogies are vertical analogies. For Hesse, vertical analogies hold the key for the construction of new theories.

Analogies play several roles in science. Not only do they contribute to discovery but they also play a role in the development and evaluation of scientific theories. Current discussions about analogy and discovery have expanded and refined Hesse’s approach in various ways. Some philosophers have developed criteria for evaluating analogy arguments (Bartha 2010). Other work has identified highly significant analogies that were particularly fruitful for the advancement of science (Holyoak and Thagard 1996: 186–188; Thagard 1999: chapter 9). The majority of analysts explore the features of the cognitive mechanisms through which aspects of a familiar domain or source are applied to an unknown target domain in order to understand what is unknown. According to the influential multi-constraint theory of analogical reasoning developed by Holyoak and Thagard, the transfer processes involved in analogical reasoning (scientific and otherwise) are guided or constrained in three main ways: 1) by the direct similarity between the elements involved; 2) by the structural parallels between source and target domain; as well as 3) by the purposes of the investigators, i.e., the reasons why the analogy is considered. Discovery, the formulation of a new hypothesis, is one such purpose.

“In vivo” investigations of scientists reasoning in their laboratories have not only shown that analogical reasoning is a key component of scientific practice, but also that the distance between source and target depends on the purpose for which analogies are sought. Scientists trying to fix experimental problems draw analogies between targets and sources from highly similar domains. In contrast, scientists attempting to formulate new models or concepts draw analogies between less similar domains. Analogies between radically different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in terms of model-based reasoning. The starting point of this approach is the notion that much of human reasoning, including probabilistic and causal reasoning as well as problem solving takes place through mental modeling rather than through the application of logic or methodological criteria to a set of propositions (Johnson-Laird 1983; Magnani et al. 1999; Magnani and Nersessian 2002). In model-based reasoning, the mind constructs a structural representation of a real-world or imaginary situation and manipulates this structure. In this perspective, conceptual structures are viewed as models and conceptual innovation as constructing new models through various modeling operations. Analogical reasoning—analogical modeling—is regarded as one of three main forms of model-based reasoning that appear to be relevant for conceptual innovation in science. Besides analogical modeling, visual modeling and simulative modeling or thought experiments also play key roles (Nersessian 1992, 1999, 2009). These modeling practices are constructive in that they aid the development of novel mental models. The key elements of model-based reasoning are the call on knowledge of generative principles and constraints for physical models in a source domain and the use of various forms of abstraction. Conceptual innovation results from the creation of new concepts through processes that abstract and integrate source and target domains into new models (Nersessian 2009).

Some critics have argued that despite the large amount of work on the topic, the notion of mental model is not sufficiently clear. Thagard seeks to clarify the concept by characterizing mental models in terms of neural processes (Thagard 2010). In his approach, mental models are produced through complex patterns of neural firing, whereby the neurons and the interconnections between them are dynamic and changing. A pattern of firing neurons is a representation when there is a stable causal correlation between the pattern or activation and the thing that is represented. In this research, questions about the nature of model-based reasoning are transformed into questions about the brain mechanisms that produce mental representations.

The above sections again show that the study of scientific discovery integrates different approaches, combining conceptual analysis of processes of knowledge generation with empirical work on creativity, drawing heavily and explicitly on current research in psychology and cognitive science, and on in vivo laboratory observations, as well as brain imaging techniques (Kounios & Beeman 2009, Thagard & Stewart 2011).

Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language (Hempel 1985: 119–120). It cannot design new experiments, instruments, or methods. Subsequent computational research on scientific discovery was driven by the motivation to contribute computational tools to aid scientists in their research (Addis et al. 2016). It appears that computational methods can be used to generate new results leading to refereed scientific publications in astrophysics, cancer research, ecology, and other fields (Langley 2000). However, the philosophical discussion has continued about the question of whether these methods really generate new knowledge or whether they merely speed up data processing. It is also still an open question whether data-intensive science is fundamentally different from traditional research, for instance regarding the status of hypothesis or theory in data-intensive research (Pietsch 2015).

In the wake of recent developments in machine learning, some older discussions about automated discovery have been revived. The availability of vastly improved computational tools and software for data analysis has stimulated new discussions about computer-generated discovery (see Leonelli 2020). It is largely uncontroversial that machine learning tools can aid discovery, for instance in research on antibiotics (Stokes et al, 2020). The notion of “robot scientist” is mostly used metaphorically, and the vision that human scientists may one day be replaced by computers – by successors of the laboratory automation systems “Adam” and “Eve”, allegedly the first “robot scientists” – is evoked in writings for broader audiences (see King et al. 2009, Williams et al. 2015, for popularized descriptions of these systems), although some interesting ethical challenges do arise from “superhuman AI” (see Russell 2021). It also appears that, on the notion that products of creative acts are both novel and valuable, AI systems should be called “creative,” an implication which not all analysts will find plausible (Boden 2014)

Philosophical analyses focus on various questions arising from the processes involving human-machine complexes. One issue relevant to the problem of scientific discovery arises from the opacity of machine learning. If machine learning indeed escapes human understanding, how can we be warranted to say that knowledge or understanding is generated by deep learning tools? Might we have reason to say that humans and machines are “co-developers” of knowledge (Tamaddoni-Nezhad et al. 2021)?

New perspectives on scientific discovery have also opened up in the context of social epistemology (see Goldman & O’Connor 2021). Social epistemology investigates knowledge production as a group process, specifically the epistemic effects of group composition in terms of cognitive diversity and unity and social interactions within groups or institutions such as testimony and trust, peer disagreement and critique, and group justification, among others. On this view, discovery is a collective achievement, and the task is to explore how assorted social-epistemic activities or practices have an impact on the knowledge generated by groups in question. There are obvious implications for debates about scientific discovery of recent research in the different branches of social epistemology. Social epistemologists have examined individual cognitive agents in their roles as group members (as providers of information or as critics) and the interactions among these members (Longino 2001), groups as aggregates of diverse agents, or the entire group as epistemic agent (e.g., Koons 2021, Dragos 2019).

Standpoint theory, for instance, explores the role of outsiders in knowledge generation, considering how the sociocultural structures and practices in which individuals are embedded aid or obstruct the generation of creative ideas. According to standpoint theorists, people with standpoint are politically aware and politically engaged people outside the mainstream. Because people with standpoint have different experiences and access to different domains of expertise than most members of a culture, they can draw on rich conceptual resources for creative thinking (Solomon 2007).

Social epistemologists examining groups as aggregates of agents consider to what extent diversity among group members is conducive to knowledge production and whether and to what extent beliefs and attitudes must be shared among group members to make collective knowledge possible (Bird 2014). This is still an open question. Some formal approaches to model the influence of diversity on knowledge generation suggest that cognitive diversity is beneficial to collective knowledge generation (Weisberg and Muldoon 2009), but others have criticized the model (Alexander et al (2015), see also Thoma (2015) and Poyhönen (2017) for further discussion).

This essay has illustrated that philosophy of discovery has come full circle. Philosophy of discovery has once again become a thriving field of philosophical study, now intersecting with, and drawing on philosophical and empirical studies of creative thinking, problem solving under uncertainty, collective knowledge production, and machine learning. Recent approaches to discovery are typically explicitly interdisciplinary and integrative, cutting across previous distinctions among hypothesis generation and theory building, data collection, assessment, and selection; as well as descriptive-analytic, historical, and normative perspectives (Danks & Ippoliti 2018, Michel 2021). The goal no longer is to provide one overarching account of scientific discovery but to produce multifaceted analyses of past and present activities of knowledge generation in all their complexity and heterogeneity that are illuminating to the non-scientist and the scientific researcher alike.

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abduction | analogy and analogical reasoning | cognitive science | epistemology: social | knowledge: analysis of | Kuhn, Thomas | models in science | Newton, Isaac: Philosophiae Naturalis Principia Mathematica | Popper, Karl | rationality: historicist theories of | scientific method | scientific research and big data | Whewell, William

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Scientific Method: Role and Importance Essay

1. introduction.

What then is the scientific method and what does it do? There is no clear and defining answer to this, but in a nutshell, the scientific method is the procedure by which scientists, communally and over periods of time, endeavored to assemble an accurate (that is reliable, consistent and non-arbitrary) representation of the world. The scientific method has been extremely successful in bringing the world out of its state of primitive knowledge and is perhaps the one characteristic that best distinguishes man from other animals. The world in which we know today is a result of the method being used, and all our achievements are according to Bacon, a result of this method. The scientific method is more than just a way of discovering ideas. It is a way of correcting our mistakes and the only definite way we have of separating our thoughts from the truth. The scientific method is the only way to ask and answer scientific questions by examining the world around us. From this, the scientific method is used to judge the merit of other methods. Thus, the method has a twofold function; it is the measure of the validity and worth of all other methods, and it is the way of finding out which method is the best.

1.1 Definition of the Scientific Method

The scientific method is defined as the steps scientists follow to create a logical, intelligent answer to questions. It is a complex method used instead of common sense to arrive at a solution. There are six well-defined steps; the first being stating the problem. This is the question that the scientist wants to know the answer to. The second step is the formation of a hypothesis. A hypothesis is an intelligent guess based on the problem that is proposed to be the solution to the problem. Step three is the designing of an experiment. This experiment is to prove or disprove the hypothesis. This is done by having a control group, which is used as a standard of comparison. Following this, a conclusion is formed if the data from the experiment has proven or disproven the hypothesis. If the hypothesis is proven true after a number of experiments, it is then considered to be a theory or law. This is a basic explanation of the scientific method, and I will be explaining it in a more detailed manner throughout the essay. Marie Curie once said, "All my life through, the new sights of nature made me rejoice like a child." People such as Curie are in every way scientists. Every day we are faced with different problems, and we often casually come up with a solution. For example, when offered lemon or vanilla ice cream, one may prefer vanilla and say it is better. This is an observation that has led to a casual conclusion. This is almost the same manner that a scientist uses to draw a hypothesis. The scientist, however, quickly moves to testing his hypothesis, while the average person may be too lazy. This is the main difference between the scientific method and common sense. Lemon ice cream is the control group, and the vanilla ice cream is the test group. If the person finds out that he enjoyed the vanilla ice cream more, then he has come to a solution. Although it is a question of relatively no importance, this is the very way that astronauts have proven weightlessness.

1.2 Historical Background

The scientific method actually began in the early 17th century when Galileo (1564-1642) and Francesco Maria Grimaldi (1618-1663) discovered that they could not adequately describe the phenomenon of light passing through different media with the current model of light that was in existence at that time, namely the emission theory. Although this was not a widespread adoption of the new method of inquiry, it is an example of a classic experiment, which is an integral part of the scientific method. It was not until the late 19th and early 20th centuries that the method of hypothesis-prediction-confirmation began to take its current form, by a series of critical experiments in the field of gravitation and general relativity. The understanding of the details of gravitation by the scientific community has evolved through the use of the scientific method and culminated in the current consensus of the theory of general relativity. This is in contrast to the previous acceptance of Newtonian gravitation as an absolute force acting at a distance. The next important step in the scientific method was the controlled experiment. The controlled experiment developed when it was realized that variables in an experiment needed to be controlled. This was first done by a Flemish Physician Jan Baptista van Helmont (1579-1644) when trying to determine where plants get their mass from. He performed an experiment in which he grew a willow tree in a weighed amount of soil and supplied it only with rain and groundwater. After five years, he weighed the tree plus the dirt and the difference in weight of the tree and dirt (which he assumed was negligible) was the weight of the water. He found that the willow tree weighed approximately 75 kgs and the dry soil was the same weight that he had started with. This showed him that the mass of the tree was derived from water and not the soil, thus proving his hypothesis. This rich history of the scientific method has led to an increasingly rigorous method of inquiry, and it is the aim of this essay to show its importance in the field of scientific research.

1.3 Purpose of the Essay

A study was done by Harvard on the use of the scientific method in reading the popular news magazine, The Scientist. It showed that stories were failed by its test on implicitly confirming them. This, according to the philosophers of science, is precisely what gives the scientific method its claim to providing a better understanding of the world than any other method. They regard the requirement to be explicit about one's theories and to test them as essential. Since not until one is explicit about a theory can it be critically evaluated, and it isn't really tested until it has survived attempts to show it false. Our primary goal should not be testing theories but achieving improvements in understanding the world. However, if we take this theory seriously, we may take the view that the understanding will be then when there are theories that are well-tested and hard to refute. At any given time, there are many competing theories about the same arrangement of evidence. So scientists concentrate on their time on improving these theories and only in times of crisis, when a competing theory has produced surprising explanatory successes, do they seriously try to compare this with the old theory by testing them. This is what happened between Newton and Einstein, who didn't refute Newton's theory of gravity after experiments in principle but offered a new, better theory that was hard to produce and so in detail comparing the old theory.

2. Key Steps of the Scientific Method

Initially, a scientist will develop a stance based on the status quo of the subject in question. From this, they will develop an educated guess as to why an event occurs. This is known as a hypothesis. A hypothesis is largely based on prior knowledge of the subject and can be used to make predictions. The next stage involves testing the hypothesis. It is at this time that a scientist will seek to ask others within the same field whether the ideas are "good" and will be awarded funding for the experiment. The funding-dependent nature of modern-day science means that this step is often revisited after experimentation is performed. Step 3 involves the testing and retesting of the hypothesis, provides an answer to the question, and guides the scientist toward the need for further research. The final steps of the scientific method involve the analysis of the data obtained by experimentation and the formulation of a conclusion, often leading to communication of the results to the scientific community.

2.1 Observation and Question Formulation

A scientist's work first begins when a question is asked. This question is informed by observation of the world around. Observation and questioning are two interrelated components of the first step of the scientific method. Observation is viewing an occurrence without involving oneself in it. It is a factual and measurable event. A scientist must be as objective as possible. If the event is a natural phenomenon, it cannot be contrived, and a scientist must look for occurrences in nature to develop a question. A question comes after an observation is made. This is a testable and measurable inquiry and should narrow down the scope of the observation to a point where it can be explained by the scientific method. Although observation and questioning are deemed as the first step of the scientific method, it can be argued that the science method of a particular investigation began when a scientist first identified a problem or when a scientist first made a mental note of a haphazard occurrence of events without actually stating a question at the time. In the later case, observation and questioning can be viewed as a continuing recurring process as a haphazard event may lead to a mental note from a scientist who may later develop a question to explain the event.

2.2 Hypothesis Development

A hypothesis is more than just an educated guess. It is a prediction or explanation that is tested by an experiment. First, a scientist must formulate a research question that she wants to answer. The question should be clear and precise. Next, the scientist makes an educated guess, or hypothesis, as to the possible answer to the question. The hypothesis is a simple statement that defines what the scientist expects to happen in his experiment. This should be based on previous knowledge, and most importantly, the hypothesis should be testable. If a question cannot be tested, there can be no research analyses or results. The purpose of the hypothesis is not to arrive at the correct answer to the question, but to provide a logical and realistic foundation for the research. Step 2.2 is included in all types of scientific research, though it may be labeled differently depending on the field of study. The term "hypothesis" is often used in an objective, systematic proposal trying to explain something. In a more basic or exploratory research project, the hypothesis may be formed after the research has been thoroughly defined. This step remains a key feature that defines the difference between a scientific and an opinion-based project, as the statement proposes our understanding of a question that can be tested in a logical manner.

2.3 Experimentation and Data Collection

In this step, a scientist tests a hypothesis using a specific method. An experiment is conducted and its results analyzed. Despite popular opinion, an experiment does not have to be conducted in a laboratory. The key attribute of an experiment is the control over the test conditions. A good experiment will have a control group and a test group; the control group is subjected to the same conditions as the test group, except for one key single variable. This variable is the factor being tested in the experiment. By isolating the variable, the scientist can attribute any change in the results to the tested factor. If the results are positive, implying the hypothesis is correct, the experiment may be repeated in order to assure the results are reproducible. There are two main types of experiments, these being inductive and deductive. Inductive experiments are used to identify a trend or pattern and are generally used to develop a hypothesis. Deductive experiments are used to test the validity of a hypothesis. The data from a deductive experiment is often used to disprove a hypothesis rather than to prove it. Both types of experiments can be useful to the testing process of a hypothesis.

2.4 Analysis and Interpretation of Results

The results obtained from the test are analyzed using statistical methods so that the data can give a definite answer to the formulated hypothesis. Analysis of data helps determine the outcome of the experiment. If the data supports the hypothesis, then the hypothesis is "accepted". If the data does not support the hypothesis, then the hypothesis is "rejected". It is important to make sure the analysis of data is free from any bias so that the results can be interpreted in the light of testing a given hypothesis. Usually in testing the hypothesis, it is comparing the results in an experiment to those of others such as drawing a cause-effect conclusion. Comparing results between two experiments is very common in scientific method. If the results support the hypothesis, then the scientist has evidence to support his/her theory. Often, it is necessary to conduct further testing to verify the theory. If the results do not support the hypothesis, then the scientist must either form a new hypothesis to test or conclude that the testing has not provided a true answer (in which case the scientist must examine the test and retest the hypothesis). Sometimes the results of an experiment can show that the hypothesis tested was actually right for an incorrect reason. This is termed as "lucking out" and may result in a scientific discovery since the unexpected results can lead into a new theory. This was the case when Rontgen discovered x-rays from his testing of cathode rays. His data did not support the hypothesis and he did not know the cause of an unexplainable glow that he obtained. Further testing led to the discovery of x-rays in an attempt to find the source of the glow.

2.5 Conclusion and Communication

At the end of the analysis, a conclusion is formed, drawing consensus from the evidence collected in the experiment. This is where the tested hypothesis is supported or refuted. If the hypothesis is not supported, a new question will arise and the steps of the scientific method will be repeated. An analysis of the nature of questions and hypotheses and the testing and observation will reveal the extreme value of the scientific method as a problem-solving tool. A successful experiment will be one that uses all the steps of the scientific method, and collects and analyses the data in a conclusive manner, resulting in either support or non-support for the hypothesis. The more formal and complete an experiment, the more conclusive will be the results. These can then be successfully communicated to others. The communication of experimental results whether they are support or non-support for a hypothesis, in the clearest and most efficient manner, is an extremely important but often neglected part of scientific process and progress. The clearer the communication of results, the more useful they are to others who may have similar questions and hypotheses and who are attempting to make further experiments or analyses in the same area.

3. Applications of the Scientific Method

When one looks at scientific research, problem solving and testing, and policy development, one can easily see how the application of scientific methods can be very handy. Science requires a systematic approach in order to answer questions about the world around it. From asking a question, to developing a method for testing, and possibly even testing future implications of the results, the scientific method is a never-ending cycle of thinking and analyzing. The scientist is always thinking of the next step in improving an idea in order to solve a question. Testing is only a part of the method. The ability to reason and think critically about an issue is a valuable skill. An example of this can be seen in problem solving. When encountering a problem, be it in the field of computer programming or in a relationship with a friend, one can benefit from applying the scientific method. By taking the steps of identifying the problem, discussing hypotheses and possible solutions, then testing each solution and possibly coming up with more problems along the way, one can promote a more effective way of critical thinking and troubleshooting. Although the concept of testing ideas is something very familiar to the field of science, the practice of it is a skill that can be used in many areas and can benefit an individual in determining the best solution to a problem.

3.1 Scientific Research

It is known that all good research begins with a question or a problem. The way to truly answer each question is to use specific methods to determine an answer. The best method known is to use the scientific method of research, which has six basic steps: asking a question, doing background research, constructing a hypothesis, testing the hypothesis with an experiment, analyzing the data, and drawing a conclusion. Finally, communicating the results. The process of using this method helps to provide a solid answer to the question in the end. It is likely that the answer may simply lead to another question, which is great because when this occurs, the scientific method can be used all over again, and in the end, there will always be a solid answer to the problem at hand. On the other hand, using the scientific method can sometimes have its flaws. A well-known advantage of the scientific method is that the data obtained is much clearer for other researchers to see and can be proven true, compared to data that is obtained by using methods of non-scientific research. This will increase researchers' confidence in the findings and also accelerate the accumulation of knowledge. A method of empirical research has a huge advantage over non-scientific methods, which often make conclusions based on limited evidence. This should make it easier to solve problems since there would be a clearer understanding of the question and solid evidence to lead to a conclusion.

3.2 Problem Solving in Various Fields

A problem consists of an undesirable situation and a desired situation. Problem solving involves the diagnosis of the situation, the selection of the best alternatives, and the prediction of the outcome of each alternative. It also involves the actual decision making, the implementation, and the evaluation of the decision and the problem. Scientists form hypotheses regarding the cause of the undesirable situation. They then systematically test the hypotheses to determine if any are valid. Usually, this will involve some manipulation of the situation. It might be most effective for a scientist to randomly assign some subjects to a control group, which will ascertain the current state of the situation, and an experimental group, which will have the manipulated treatment applied. Independent and dependent variables are identified and controlled. The scientist can then examine the correlation between the independent variable and the undesirable situation and examine if changing the independent variable will also change the dependent variable. The activity can be similar to a pseudo experiment, like testing to see if increased political stability in a third world country will increase the standard of living of its citizens, or a historical study, like trying to determine the cause of an increase or decrease in the street crime rate. The more conclusive the evidence, the more the hypotheses are supported or refuted. If a supported hypothesis presents an effective solution to the problem, the scientist may use it to implement a decision. Acting the same as he predicted the alternative outcomes would act, he can monitor and evaluate the situation to see if the alternative actually improves the situation.

3.3 Decision Making and Policy Development

In the previous section, we observed that problem solving was a large part of the methodology used by scientists and researchers alike. They identify problems and yet again, using logic, attempt to solve them in the most efficient manner possible. In many cases, problems lead to decisions that need to be made. Decision making is the selection of a procedure to be implemented in order to resolve some form of problem. The scientific method is often applied to decision making. The steps are similar to those that are used in solving a problem, which make it easier to come to the most effective solution. The decisions made are based on a set of conditions and they attempt to match the condition with an alternative. Usually, the best decision is the one that selects the alternative which will bring most fulfillment to the condition. If the result is not the one desired, the decision maker will re-evaluate the alternative and condition. In policy development, decisions are made in attempts to change the current state of problems or to avoid a potential problem. A policy is a decision that is implemented by a group or an organization, which is intended to carry out a specific course of action. It is very similar to a decision, instead it involves many decisions following the selection of a preferred alternative. The aim is to change a set of undesirable conditions to more desirable ones. This can be achieved by using the alternatives available to bring a resolution to the conditions. Both decisions and policies can have effects. If the effects are good ones, it can be said the decision was a fruitful one. However, some decisions have many alternatives with unpredictable results. This can be risky and often, to avoid such risk, a decision maker will attempt to simulate the decision in hope to make the optimal move.

4. Criticisms and Limitations of the Scientific Method

The scientific method is a very powerful tool to increase understanding of a complex issue. However, the scientific method is by no means free from limitations or criticism. Predictably, the scientific method receives the strongest criticism from disciplines outside of the natural sciences, and its objectivity is held into question. Particularly in the social sciences, the scientific method has been accused of lacking objectivity or being unable to gain full understanding of phenomena. Objectivity is heavily tied in with the concept of bias, which is the influence of a person's existing knowledge or beliefs upon his/her findings. It has been said that there is no research without bias, and the line between the unbiased pursuance of knowledge and testing of a preconceived hypothesis is sometimes a fine one. An example of bias discussed in philosophy is the response by physicists to the problems of light as a wave or particle. At no time in the history of philosophy did such a question arise in the mind of a philosopher, and yet the reason it was addressed in physics is simply that philosophers of physics were not making progress and physicists felt a need to study the nature of the problem. This biased the physicist's findings as they had mathematical data to prove that light was a wave and stopped researching when they encountered it, despite the fact that a particle theory had equal explanatory power. Said Kuhn, the result of the 20th century research into the nature of light was a 400 years accumulation of knowledge in which no real progress was ever made. This is an extreme case, but it is hard to say that any scientist has not at some time tested a hypothesis built from existing knowledge and had that bias affect their results.

4.1 Subjectivity and Bias

One of the most well-known criticisms of the scientific method is the presence of human subjectivity and bias. The scientific method was developed in part to escape such things, and it is therefore counter-effective if a scientist allows his or her personal beliefs to determine the outcome of an experiment. For this reason, the second step of the scientific method is often the hardest to uphold. Here, a scientist must form a hypothesis - an informed statement about the likely outcome of an experiment. This hypothesis is formed from previous knowledge which may in itself be biased, the issue is therefore how to decide whether a biased hypothesis is worth pursuing and testing. If the scientist decides that it isn't, the experiment has already fallen victim to bias. A similar problem lies in the testing stage. If a scientist holds a biased view, he or she may only notice results which agree with their hypothesis and ignore results which invalidate it. In extreme cases, a scientist may manipulate the results of an experiment so that they better fit the hypothesis, or even fabricate results. Such fraud can be very damaging, and in recent years there have been many high-profile cases of scientists found guilty of such acts. In an attempt to combat this, the practice of blind testing is becoming more common. This is an experiment where the tester does not know what he is testing, or what the expected outcome is. This prevents his biases from affecting the outcome. The most common type of blind testing is a double-blind test, where neither the tester nor the subjects know the details of the experiment. The issue of bias in the scientific method is a difficult problem, and as yet there is no perfect solution. However, it is clear that by merely recognizing the presence of bias and the damage it can cause, the scientific community has taken a step in the right direction.

4.2 Ethical Considerations

The scientific method is applied to such a wide variety of activities that it would be difficult to pinpoint a single set of universal ethical considerations. However, broad categories can be identified. First, there is the question of the morality of the activity being studied. For example, research into the reproductive habits of a low-income community with the aim of limiting their birth rate is an activity which carries many value assumptions and would be widely regarded as immoral. Scientists are usually in agreement as to what activities are immoral - where they fall down is on what constitutes each activity. In the previous example, the researchers might believe that they are contributing to a better quality of life for the community in question. Their study is not necessarily an unbiased look at the habits in question: it might also fall foul of the second activity in the category to be discussed: research using human subjects. A great deal of scientific activity passes by the criteria that dictates what does and does not constitute research using human subjects. In recent decades, medical research has been severely limited by this criteria, with much of it being forced underground into less developed countries. During the mid 20th century, drug companies in developed countries conducted research into the long-term effects of new drugs by administering them to prison inmates. This too would now be viewed as a clear breach of the criteria on research using human subjects, despite the fact that the inmates would often be better off than if they had not taken the drug. Third in this category is the notion that the end results of scientific activities should in some way be beneficial for society. This also has value assumptions attached to it: what is beneficial, and for whom? An activity perceived as beneficial for society in general might still be viewed as detrimental to certain groups within society. Taking the example of a scientific study comparing the intelligence levels of different races, a highly contentious issue, the researchers might believe that their study is beneficial for the races in question, suggesting that better education policies could be formulated if the reasons for differing educational attainment between races were better understood. However, it is difficult to see how the results of this study would not reinforce existing racial stereotypes. All value assumptions aside, the scientific method has often fallen short of this criterion. The development of new weapons technology is an activity which has often been of great benefit to the country commissioning it, but this does not necessarily mean that it was arrived at using a scientific methodology designed to maximize benefit for society in general.

4.3 Complexity of Real-World Problems

Complexity is another criticism labeled at the scientific method. This criticism is usually associated with trying to utilize the scientific method in the study and application of different sciences such as economics, biology, and physics. Critics argue that the scientific method is reductionist. They say that the construction of a simplified model of a complex phenomenon defines away too much of the problem to fit the simple structure the model embodies. The scientist tests the model and if it is not validated, or in the case of economic models, does not behave as expected, the scientist makes revisions until the model does work. The point at which the model works is often a long way from the original real-world problem. Often it is so far away that the problem it was originally addressing has been forgotten or is no longer relevant. At this point, the scientist has a paradigm. The problem with this is that the paradigm may not be valid or applicable to the real world, but the scientist has invested so much time and effort into the paradigm that it becomes very difficult to give it up. This whole process can have far-reaching effects and if the scientist has embodied the paradigm into policymaking, the effects can be disastrous. An example is the stagflation of the 1970s. Keynesian economists had developed an almost globally accepted paradigm that their policies could keep the trade-off between inflation and unemployment at any point. Keynesian models were simulated and were working, however, when the OPEC oil shocks occurred, the Keynesian policies did not have the expected results. But because the effort put into the model had been so great and the paradigm so widely accepted, it was not given up and Keynesian policies were followed to try to fix the new problems, only making things worse. The building and embodiment of a paradigm is said to be the only way a model can work, but it is extremely difficult to test for the validity of the paradigm and because of this, there is a risk of the model becoming an exercise in theoretical self-indulgence.

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Scientific Methods

What is scientific method.

The Scientific method is a process with the help of which scientists try to investigate, verify, or construct an accurate and reliable version of any natural phenomena. They are done by creating an objective framework for the purpose of scientific inquiry and analysing the results scientifically to come to a conclusion that either supports or contradicts the observation made at the beginning.

Scientific Method Steps

The aim of all scientific methods is the same, that is, to analyse the observation made at the beginning. Still, various steps are adopted per the requirement of any given observation. However, there is a generally accepted sequence of steps in scientific methods.

• Observation and formulation of a question:  This is the first step of a scientific method. To start one, an observation has to be made into any observable aspect or phenomena of the universe, and a question needs to be asked about that aspect. For example, you can ask, “Why is the sky black at night? or “Why is air invisible?”
• Data Collection and Hypothesis:  The next step involved in the scientific method is to collect all related data and formulate a hypothesis based on the observation. The hypothesis could be the cause of the phenomena, its effect, or its relation to any other phenomena.
• Testing the hypothesis:  After the hypothesis is made, it needs to be tested scientifically. Scientists do this by conducting experiments. The aim of these experiments is to determine whether the hypothesis agrees with or contradicts the observations made in the real world. The confidence in the hypothesis increases or decreases based on the result of the experiments.
• Analysis and Conclusion:  This step involves the use of proper mathematical and other scientific procedures to determine the results of the experiment. Based on the analysis, the future course of action can be determined. If the data found in the analysis is consistent with the hypothesis, it is accepted. If not, then it is rejected or modified and analysed again.

It must be remembered that a hypothesis cannot be proved or disproved by doing one experiment. It needs to be done repeatedly until there are no discrepancies in the data and the result. When there are no discrepancies and the hypothesis is proved, it is accepted as a ‘theory’.

Scientific Method Examples

Following is an example of the scientific method:

Growing bean plants:

• What is the purpose: The main purpose of this experiment is to know where the bean plant should be kept inside or outside to check the growth rate and also set the time frame as four weeks.
• Construction of hypothesis: The hypothesis used is that the bean plant can grow anywhere if the scientific methods are used.
• Executing the hypothesis and collecting the data: Four bean plants are planted in identical pots using the same soil. Two are placed inside, and the other two are placed outside. Parameters like the amount of exposure to sunlight, and amount of water all are the same. After the completion of four weeks, all four plant sizes are measured.
• Analyse the data:  While analysing the data, the average height of plants should be taken into account from both places to determine which environment is more suitable for growing the bean plants.
• Conclusion:  The conclusion is drawn after analyzing the data.
• Results:  Results can be reported in the form of a tabular form.

Frequently Asked Questions – FAQs

What is scientific method, what is hypothesis, give an example of a simple hypothesis., define complex hypothesis., what are the steps of the scientific method, what is the aim of scientific methods, state true or false: observation and formulation of a question is the third step of scientific method, explain the step: analysis and conclusion., leave a comment cancel reply.

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1.2 The Scientific Methods

Section learning objectives.

By the end of this section, you will be able to do the following:

• Explain how the methods of science are used to make scientific discoveries
• Define a scientific model and describe examples of physical and mathematical models used in physics
• Compare and contrast hypothesis, theory, and law

Teacher Support

The learning objectives in this section will help your students master the following standards:

• (A) know the definition of science and understand that it has limitations, as specified in subsection (b)(2) of this section;
• (B) know that scientific hypotheses are tentative and testable statements that must be capable of being supported or not supported by observational evidence. Hypotheses of durable explanatory power which have been tested over a wide variety of conditions are incorporated into theories;
• (C) know that scientific theories are based on natural and physical phenomena and are capable of being tested by multiple independent researchers. Unlike hypotheses, scientific theories are well-established and highly-reliable explanations, but may be subject to change as new areas of science and new technologies are developed;
• (D) distinguish between scientific hypotheses and scientific theories.

Section Key Terms

[OL] Pre-assessment for this section could involve students sharing or writing down an anecdote about when they used the methods of science. Then, students could label their thought processes in their anecdote with the appropriate scientific methods. The class could also discuss their definitions of theory and law, both outside and within the context of science.

[OL] It should be noted and possibly mentioned that a scientist , as mentioned in this section, does not necessarily mean a trained scientist. It could be anyone using methods of science.

Scientific Methods

Scientists often plan and carry out investigations to answer questions about the universe around us. These investigations may lead to natural laws. Such laws are intrinsic to the universe, meaning that humans did not create them and cannot change them. We can only discover and understand them. Their discovery is a very human endeavor, with all the elements of mystery, imagination, struggle, triumph, and disappointment inherent in any creative effort. The cornerstone of discovering natural laws is observation. Science must describe the universe as it is, not as we imagine or wish it to be.

We all are curious to some extent. We look around, make generalizations, and try to understand what we see. For example, we look up and wonder whether one type of cloud signals an oncoming storm. As we become serious about exploring nature, we become more organized and formal in collecting and analyzing data. We attempt greater precision, perform controlled experiments (if we can), and write down ideas about how data may be organized. We then formulate models, theories, and laws based on the data we have collected, and communicate those results with others. This, in a nutshell, describes the scientific method that scientists employ to decide scientific issues on the basis of evidence from observation and experiment.

An investigation often begins with a scientist making an observation . The scientist observes a pattern or trend within the natural world. Observation may generate questions that the scientist wishes to answer. Next, the scientist may perform some research about the topic and devise a hypothesis . A hypothesis is a testable statement that describes how something in the natural world works. In essence, a hypothesis is an educated guess that explains something about an observation.

[OL] An educated guess is used throughout this section in describing a hypothesis to combat the tendency to think of a theory as an educated guess.

Scientists may test the hypothesis by performing an experiment . During an experiment, the scientist collects data that will help them learn about the phenomenon they are studying. Then the scientists analyze the results of the experiment (that is, the data), often using statistical, mathematical, and/or graphical methods. From the data analysis, they draw conclusions. They may conclude that their experiment either supports or rejects their hypothesis. If the hypothesis is supported, the scientist usually goes on to test another hypothesis related to the first. If their hypothesis is rejected, they will often then test a new and different hypothesis in their effort to learn more about whatever they are studying.

Scientific processes can be applied to many situations. Let’s say that you try to turn on your car, but it will not start. You have just made an observation! You ask yourself, "Why won’t my car start?" You can now use scientific processes to answer this question. First, you generate a hypothesis such as, "The car won’t start because it has no gasoline in the gas tank." To test this hypothesis, you put gasoline in the car and try to start it again. If the car starts, then your hypothesis is supported by the experiment. If the car does not start, then your hypothesis is rejected. You will then need to think up a new hypothesis to test such as, "My car won’t start because the fuel pump is broken." Hopefully, your investigations lead you to discover why the car won’t start and enable you to fix it.

A model is a representation of something that is often too difficult (or impossible) to study directly. Models can take the form of physical models, equations, computer programs, or simulations—computer graphics/animations. Models are tools that are especially useful in modern physics because they let us visualize phenomena that we normally cannot observe with our senses, such as very small objects or objects that move at high speeds. For example, we can understand the structure of an atom using models, without seeing an atom with our own eyes. Although images of single atoms are now possible, these images are extremely difficult to achieve and are only possible due to the success of our models. The existence of these images is a consequence rather than a source of our understanding of atoms. Models are always approximate, so they are simpler to consider than the real situation; the more complete a model is, the more complicated it must be. Models put the intangible or the extremely complex into human terms that we can visualize, discuss, and hypothesize about.

Scientific models are constructed based on the results of previous experiments. Even still, models often only describe a phenomenon partially or in a few limited situations. Some phenomena are so complex that they may be impossible to model them in their entirety, even using computers. An example is the electron cloud model of the atom in which electrons are moving around the atom’s center in distinct clouds ( Figure 1.12 ), that represent the likelihood of finding an electron in different places. This model helps us to visualize the structure of an atom. However, it does not show us exactly where an electron will be within its cloud at any one particular time.

As mentioned previously, physicists use a variety of models including equations, physical models, computer simulations, etc. For example, three-dimensional models are often commonly used in chemistry and physics to model molecules. Properties other than appearance or location are usually modelled using mathematics, where functions are used to show how these properties relate to one another. Processes such as the formation of a star or the planets, can also be modelled using computer simulations. Once a simulation is correctly programmed based on actual experimental data, the simulation can allow us to view processes that happened in the past or happen too quickly or slowly for us to observe directly. In addition, scientists can also run virtual experiments using computer-based models. In a model of planet formation, for example, the scientist could alter the amount or type of rocks present in space and see how it affects planet formation.

Scientists use models and experimental results to construct explanations of observations or design solutions to problems. For example, one way to make a car more fuel efficient is to reduce the friction or drag caused by air flowing around the moving car. This can be done by designing the body shape of the car to be more aerodynamic, such as by using rounded corners instead of sharp ones. Engineers can then construct physical models of the car body, place them in a wind tunnel, and examine the flow of air around the model. This can also be done mathematically in a computer simulation. The air flow pattern can be analyzed for regions smooth air flow and for eddies that indicate drag. The model of the car body may have to be altered slightly to produce the smoothest pattern of air flow (i.e., the least drag). The pattern with the least drag may be the solution to increasing fuel efficiency of the car. This solution might then be incorporated into the car design.

Using Models and the Scientific Processes

Be sure to secure loose items before opening the window or door.

In this activity, you will learn about scientific models by making a model of how air flows through your classroom or a room in your house.

• One room with at least one window or door that can be opened
• Work with a group of four, as directed by your teacher. Close all of the windows and doors in the room you are working in. Your teacher may assign you a specific window or door to study.
• Before opening any windows or doors, draw a to-scale diagram of your room. First, measure the length and width of your room using the tape measure. Then, transform the measurement using a scale that could fit on your paper, such as 5 centimeters = 1 meter.
• Your teacher will assign you a specific window or door to study air flow. On your diagram, add arrows showing your hypothesis (before opening any windows or doors) of how air will flow through the room when your assigned window or door is opened. Use pencil so that you can easily make changes to your diagram.
• On your diagram, mark four locations where you would like to test air flow in your room. To test for airflow, hold a strip of single ply tissue paper between the thumb and index finger. Note the direction that the paper moves when exposed to the airflow. Then, for each location, predict which way the paper will move if your air flow diagram is correct.
• Now, each member of your group will stand in one of the four selected areas. Each member will test the airflow Agree upon an approximate height at which everyone will hold their papers.
• When you teacher tells you to, open your assigned window and/or door. Each person should note the direction that their paper points immediately after the window or door was opened. Record your results on your diagram.
• Did the airflow test data support or refute the hypothetical model of air flow shown in your diagram? Why or why not? Correct your model based on your experimental evidence.
• With your group, discuss how accurate your model is. What limitations did it have? Write down the limitations that your group agreed upon.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.

This Snap Lab! has students construct a model of how air flows in their classroom. Each group of four students will create a model of air flow in their classroom using a scale drawing of the room. Then, the groups will test the validity of their model by placing weathervanes that they have constructed around the room and opening a window or door. By observing the weather vanes, students will see how air actually flows through the room from a specific window or door. Students will then correct their model based on their experimental evidence. The following material list is given per group:

• One room with at least one window or door that can be opened (An optimal configuration would be one window or door per group.)
• Several pieces of construction paper (at least four per group)
• Strips of single ply tissue paper
• One tape measure (long enough to measure the dimensions of the room)
• Group size can vary depending on the number of windows/doors available and the number of students in the class.
• The room dimensions could be provided by the teacher. Also, students may need a brief introduction in how to make a drawing to scale.
• This is another opportunity to discuss controlled experiments in terms of why the students should hold the strips of tissue paper at the same height and in the same way. One student could also serve as a control and stand far away from the window/door or in another area that will not receive air flow from the window/door.
• You will probably need to coordinate this when multiple windows or doors are used. Only one window or door should be opened at a time for best results. Between openings, allow a short period (5 minutes) when all windows and doors are closed, if possible.

Answers to the Grasp Check will vary, but the air flow in the new window or door should be based on what the students observed in their experiment.

Scientific Laws and Theories

A scientific law is a description of a pattern in nature that is true in all circumstances that have been studied. That is, physical laws are meant to be universal , meaning that they apply throughout the known universe. Laws are often also concise, whereas theories are more complicated. A law can be expressed in the form of a single sentence or mathematical equation. For example, Newton’s second law of motion , which relates the motion of an object to the force applied ( F ), the mass of the object ( m ), and the object’s acceleration ( a ), is simply stated using the equation

Scientific ideas and explanations that are true in many, but not all situations in the universe are usually called principles . An example is Pascal’s principle , which explains properties of liquids, but not solids or gases. However, the distinction between laws and principles is sometimes not carefully made in science.

A theory is an explanation for patterns in nature that is supported by much scientific evidence and verified multiple times by multiple researchers. While many people confuse theories with educated guesses or hypotheses, theories have withstood more rigorous testing and verification than hypotheses.

[OL] Explain to students that in informal, everyday English the word theory can be used to describe an idea that is possibly true but that has not been proven to be true. This use of the word theory often leads people to think that scientific theories are nothing more than educated guesses. This is not just a misconception among students, but among the general public as well.

As a closing idea about scientific processes, we want to point out that scientific laws and theories, even those that have been supported by experiments for centuries, can still be changed by new discoveries. This is especially true when new technologies emerge that allow us to observe things that were formerly unobservable. Imagine how viewing previously invisible objects with a microscope or viewing Earth for the first time from space may have instantly changed our scientific theories and laws! What discoveries still await us in the future? The constant retesting and perfecting of our scientific laws and theories allows our knowledge of nature to progress. For this reason, many scientists are reluctant to say that their studies prove anything. By saying support instead of prove , it keeps the door open for future discoveries, even if they won’t occur for centuries or even millennia.

[OL] With regard to scientists avoiding using the word prove , the general public knows that science has proven certain things such as that the heart pumps blood and the Earth is round. However, scientists should shy away from using prove because it is impossible to test every single instance and every set of conditions in a system to absolutely prove anything. Using support or similar terminology leaves the door open for further discovery.

Check Your Understanding

• Models are simpler to analyze.
• Models give more accurate results.
• Models provide more reliable predictions.
• Models do not require any computer calculations.
• They are the same.
• A hypothesis has been thoroughly tested and found to be true.
• A hypothesis is a tentative assumption based on what is already known.
• A hypothesis is a broad explanation firmly supported by evidence.
• A scientific model is a representation of something that can be easily studied directly. It is useful for studying things that can be easily analyzed by humans.
• A scientific model is a representation of something that is often too difficult to study directly. It is useful for studying a complex system or systems that humans cannot observe directly.
• A scientific model is a representation of scientific equipment. It is useful for studying working principles of scientific equipment.
• A scientific model is a representation of a laboratory where experiments are performed. It is useful for studying requirements needed inside the laboratory.
• The hypothesis must be validated by scientific experiments.
• The hypothesis must not include any physical quantity.
• The hypothesis must be a short and concise statement.
• The hypothesis must apply to all the situations in the universe.
• A scientific theory is an explanation of natural phenomena that is supported by evidence.
• A scientific theory is an explanation of natural phenomena without the support of evidence.
• A scientific theory is an educated guess about the natural phenomena occurring in nature.
• A scientific theory is an uneducated guess about natural phenomena occurring in nature.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is an educated guess about a natural phenomenon.
• A hypothesis is an educated guess about natural phenomenon, while a scientific theory is an explanation of natural world with experimental support.
• A hypothesis is experimental evidence of a natural phenomenon, while a scientific theory is an explanation of the natural world with experimental support.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is experimental evidence of a natural phenomenon.

Use the Check Your Understanding questions to assess students’ achievement of the section’s learning objectives. If students are struggling with a specific objective, the Check Your Understanding will help identify which objective and direct students to the relevant content.

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Scientific Method Definition in the United States Essay (Critical Writing)

The article in question dwells upon the essence of the so-called scientific method as understood in the United States and the rest of the contemporary world. Hollinger provides insight into the way this idea developed between the 1940s and 1960s (441). The author notes that the cluster or even opposition of different ideologies enabled U.S. society to come up with the scientific method as people understand it now.

Interestingly, general societal views were very different in the middle of the twentieth century. Prejudice, the pursuit of particular goals, and bias were quite typical of both society in general and particular individual spheres like the academic world during that time. The Second World War significantly shaped the way people viewed their goals and aspirations.

First of all, the author successfully draws the reader’s attention to the meaning of the word “scientific” and traces the evolution of this idea. This part of the article makes everyone consider the way they themselves see the “scientific” approach. At present, people admit that just conducting any experiment in any way cannot be the essence of the scientific method. Instead, it it essential to conduct the research in adherence to some specific principles and values.

The researcher has to be precise and careful, ethical and open. It is vital to make sure that the researcher’s personality, experiences, and views do not affect the findings in any way. All the relevant data should be provided, irrespective of their consistency with certain ideologies or conventions. Ultimately, the truth is the primary goal of any research.

The author’s argument concerning the interaction of religions (Catholicism and Protestantism) and ideologies (communism, fascism and democracy) is eye-opening. By making this argument, it becomes clear why people chose to focus on the importance of knowledge and morality in research. Decades of authoritarian rule—such as the regimes in Germany, the USSR, Spain, and Italy—were regarded as illustrations of the need to mobilize and use all possible means to oppose those ideologies. In the United States, society, which proclaimed itself a democratic one, did not quite tolerate views that were different from the accepted ones (Hollinger 443). Segregation was not confined to African Americans, and the dominance of Americans of Anglo-Saxon descent was apparent in all spheres of life. The author makes it clear that the horrible crimes committed in the name of ideology as well as the hypocrisy of religions encouraged people to seek pure science and truth. Scholars understood that the absence of bias and the reign of pluralism could be seen as the key to success.

When reading the article, it might seem that Jews are regarded as the central ethnicity, the people who most contributed to the development of the scientific approach. The author stresses that the fact that Jews were allowed into science, art, and technology (and by extension, American culture as a whole) was one of the most important milestones in the development of the new paradigm.

Though Jewish scientists and scholars certainly did not exclusively come up with the innovations that advanced the scientific sphere, it is clear that the case of a single ethnicity paved the way to a new attitude and paradigm in the scientific world—one that transcended all other social, political, and cultural spheres. The fact that Jews were allowed into the scientific world created a valuable precedent that showed both the negative effects of any kind of bias and the benefits of plurality and transparency. Indeed, the scientific world started to become less biased and more open. This openness embraced many aspects, including the adoption of new ways and strategies and tolerance towards different groups of people in terms of gender, ethnicity, sexual orientation, and so on.

At the same time, the author seems to exaggerate the victories of the new scientific approach. Of course, after the 1960s, American society did become more open and inclusive; nonetheless, after the 1960s and up until the present, there has still existed a lot of bias toward women. To this day, many women are still struggling to prove their right to participate in the scientific world. Furthermore, various conventions still prevented scholars and scientists from being truly objective and scientific in the sense developed in the middle of the twentieth century. Thus, scholars and scientists had to choose between their moral values or beliefs and the truth unveiled during their research. More so, the struggle of values, conventions, and scientific principles still persists.

This article brings to the fore several ideas concerning contemporary society. At present, some people may forget about the importance of remaining ethical in research. Though the scientific method is still an ideal that many try to follow, it is not as valued as it used to be. One of the reasons for that devaluation is the general stability in Western society.

Ironically, because people are not as anxious about the looming threat of an ideology and because they are free to express their views, they are rather careless and can contribute to the creation of more authoritarian ideologies if they try to adjust their findings to the existing conventions. People have become accustomed to living in the world of the scientific paradigm. They do not know what life could be like without the standard of compliance with the values of the scientific approach. Therefore, it is important to make sure that this article is revisited by people around the world and that Americans (especially young people) start a lasting debate on the matter.

Another important idea that is discussed in the article is concerned with the threat of reliance on a particular ideology. The modern world is becoming quite polarized again. Of course, communism and fascism are unlikely to gain the same power as they had in the first part of the twentieth century. However, certain ideologies may entice people away from pursuing a truly scientific method. Indeed, researchers may try to slightly adjust their findings to fit the existing conventions, disregarding the truth. This seems to be the first step towards the rigid and closed world that existed in the 1940s. It is necessary to remember that the adherence to the principles of the scientific approach in all aspects of human life can help American society preserve its democratic ideology.

In conclusion, this article is a valuable resource to be discussed in establishments of higher education and other settings. The author explains how the contemporary scientific approach was developed by revealing that the background of this currently accepted paradigm is deeply rooted in the struggle of different ideologies. The modern scientific world is supported by rules and values that include transparency, reliability, and credibility. When modern people start violating these principles, it can lead to significant adverse effects. Hence, it is essential to turn back to the debate of the scientific approach and its origins, as this will facilitate the adherence to the values of the scientific paradigm.

Works Cited

Hollinger, David A. “Science as a Weapon in Kulturkampfe in the United States During and After World War II”. Isis 86.3 (1995): 440-454. Print.

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IvyPanda. (2020, August 18). Scientific Method Definition in the United States. https://ivypanda.com/essays/scientific-method-definition-in-the-united-states/

"Scientific Method Definition in the United States." IvyPanda , 18 Aug. 2020, ivypanda.com/essays/scientific-method-definition-in-the-united-states/.

IvyPanda . (2020) 'Scientific Method Definition in the United States'. 18 August.

IvyPanda . 2020. "Scientific Method Definition in the United States." August 18, 2020. https://ivypanda.com/essays/scientific-method-definition-in-the-united-states/.

1. IvyPanda . "Scientific Method Definition in the United States." August 18, 2020. https://ivypanda.com/essays/scientific-method-definition-in-the-united-states/.

Bibliography

IvyPanda . "Scientific Method Definition in the United States." August 18, 2020. https://ivypanda.com/essays/scientific-method-definition-in-the-united-states/.

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