what is an alternative hypothesis ap biology

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AP®︎/College Statistics

Course: ap®︎/college statistics   >   unit 10.

• Idea behind hypothesis testing

Examples of null and alternative hypotheses

• Writing null and alternative hypotheses
• P-values and significance tests
• Comparing P-values to different significance levels
• Estimating a P-value from a simulation
• Estimating P-values from simulations
• Using P-values to make conclusions

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Video transcript

AP® Biology

The chi square test: ap® biology crash course.

• The Albert Team
• Last Updated On: March 7, 2024

The statistics section of the AP® Biology exam is without a doubt one of the most notoriously difficult sections. Biology students are comfortable with memorizing and understanding content, which is why this topic seems like the most difficult to master. In this article,  The Chi Square Test: AP® Biology Crash Course , we will teach you a system for how to perform the Chi Square test every time. We will begin by reviewing some topics that you must know about statistics before you can complete the Chi Square test. Next, we will simplify the equation by defining each of the Chi Square variables. We will then use a simple example as practice to make sure that we have learned every part of the equation. Finally, we will finish with reviewing a more difficult question that you could see on your AP® Biology exam .

Null and Alternative Hypotheses

As background information, first you need to understand that a scientist must create the null and alternative hypotheses prior to performing their experiment. If the dependent variable is not influenced by the independent variable , the null hypothesis will be accepted. If the dependent variable is influenced by the independent variable, the data should lead the scientist to reject the null hypothesis . The null and alternative hypotheses can be a difficult topic to describe. Let’s look at an example.

Consider an experiment about flipping a coin. The null hypothesis would be that you would observe the coin landing on heads fifty percent of the time and the coin landing on tails fifty percent of the time. The null hypothesis predicts that you will not see a change in your data due to the independent variable.

The alternative hypothesis for this experiment would be that you would not observe the coins landing on heads and tails an even number of times. You could choose to hypothesize you would see more heads, that you would see more tails, or that you would just see a different ratio than 1:1. Any of these hypotheses would be acceptable as alternative hypotheses.

Defining the Variables

Now we will go over the Chi-Square equation. One of the most difficult parts of learning statistics is the long and confusing equations. In order to master the Chi Square test, we will begin by defining the variables.

This is the Chi Square test equation. You must know how to use this equation for the AP® Bio exam. However, you will not need to memorize the equation; it will be provided to you on the AP® Biology Equations and Formulas sheet that you will receive at the beginning of your examination.

Now that you have seen the equation, let’s define each of the variables so that you can begin to understand it!

•   X 2  :The first variable, which looks like an x, is called chi squared. You can think of chi like x in algebra because it will be the variable that you will solve for during your statistical test. •   ∑ : This symbol is called sigma. Sigma is the symbol that is used to mean “sum” in statistics. In this case, this means that we will be adding everything that comes after the sigma together. •   O : This variable will be the observed data that you record during your experiment. This could be any quantitative data that is collected, such as: height, weight, number of times something occurs, etc. An example of this would be the recorded number of times that you get heads or tails in a coin-flipping experiment. •   E : This variable will be the expected data that you will determine before running your experiment. This will always be the data that you would expect to see if your independent variable does not impact your dependent variable. For example, in the case of coin flips, this would be 50 heads and 50 tails.

The equation should begin to make more sense now that the variables are defined.

Working out the Coin Flip

We have talked about the coin flip example and, now that we know the equation, we will solve the problem. Let’s pretend that we performed the coin flip experiment and got the following data:

Now we put these numbers into the equation:

Heads (55-50) 2 /50= .5

Tails (45-50) 2 /50= .5

Lastly, we add them together.

c 2 = .5+.5=1

Now that we have c 2 we must figure out what that means for our experiment! To do that, we must review one more concept.

Degrees of Freedom and Critical Values

Degrees of freedom is a term that statisticians use to determine what values a scientist must get for the data to be significantly different from the expected values. That may sound confusing, so let’s try and simplify it. In order for a scientist to say that the observed data is different from the expected data, there is a numerical threshold the scientist must reach, which is based on the number of outcomes and a chosen critical value.

Let’s return to our coin flipping example. When we are flipping the coin, there are two outcomes: heads and tails. To get degrees of freedom, we take the number of outcomes and subtract one; therefore, in this experiment, the degree of freedom is one. We then take that information and look at a table to determine our chi-square value:

We will look at the column for one degree of freedom. Typically, scientists use a .05 critical value. A .05 critical value represents that there is a 95% chance that the difference between the data you expected to get and the data you observed is due to something other than chance. In this example, our value will be 3.84.

Coin Flip Results

In our coin flip experiment, Chi Square was 1. When we look at the table, we see that Chi Square must have been greater than 3.84 for us to say that the expected data was significantly different from the observed data. We did not reach that threshold. So, for this example, we will say that we failed to reject the null hypothesis.

The best way to get better at these statistical questions is to practice. Next, we will go through a question using the Chi Square Test that you could see on your AP® Bio exam.

AP® Biology Exam Question

This question was adapted from the 2013 AP® Biology exam.

In an investigation of fruit-fly behavior, a covered choice chamber is used to test whether the spatial distribution of flies is affected by the presence of a substance placed at one end of the chamber. To test the flies’ preference for glucose, 60 flies are introduced into the middle of the choice chamber at the insertion point. A ripe banana is placed at one end of the chamber, and an unripe banana is placed at the other end. The positions of flies are observed and recorded after 1 minute and after 10 minutes. Perform a Chi Square test on the data for the ten minute time point. Specify the null hypothesis and accept or reject it.

Okay, we will begin by identifying the null hypothesis . The null hypothesis would be that the flies would be evenly distributed across the three chambers (ripe, middle, and unripe).

Next, we will perform the Chi-Square test just like we did in the heads or tails experiment. Because there are three conditions, it may be helpful to use this set up to organize yourself:

Ok, so we have a Chi Square of 48.9. Our degrees of freedom are 3(ripe, middle, unripe)-1=2. Let’s look at that table above for a confidence variable of .05. You should get a value of 5.99. Our Chi Square value of 48.9 is much larger than 5.99 so in this case we are able to reject the null hypothesis. This means that the flies are not randomly assorting themselves, and the banana is influencing their behavior.

The Chi Square test is something that takes practice. Once you learn the system of solving these problems, you will be able to solve any Chi Square problem using the exact same method every time! In this article, we have reviewed the Chi Square test using two examples. If you are still interested in reviewing the bio-statistics that will be on your AP® Biology Exam, please check out our article The Dihybrid Cross Problem: AP® Biology Crash Course . Let us know how studying is going and if you have any questions!

Need help preparing for your AP® Biology exam?

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9.1: Null and Alternative Hypotheses

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The actual test begins by considering two hypotheses . They are called the null hypothesis and the alternative hypothesis . These hypotheses contain opposing viewpoints.

\(H_0\): The null hypothesis: It is a statement of no difference between the variables—they are not related. This can often be considered the status quo and as a result if you cannot accept the null it requires some action.

\(H_a\): The alternative hypothesis: It is a claim about the population that is contradictory to \(H_0\) and what we conclude when we reject \(H_0\). This is usually what the researcher is trying to prove.

Since the null and alternative hypotheses are contradictory, you must examine evidence to decide if you have enough evidence to reject the null hypothesis or not. The evidence is in the form of sample data.

After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are "reject \(H_0\)" if the sample information favors the alternative hypothesis or "do not reject \(H_0\)" or "decline to reject \(H_0\)" if the sample information is insufficient to reject the null hypothesis.

\(H_{0}\) always has a symbol with an equal in it. \(H_{a}\) never has a symbol with an equal in it. The choice of symbol depends on the wording of the hypothesis test. However, be aware that many researchers (including one of the co-authors in research work) use = in the null hypothesis, even with > or < as the symbol in the alternative hypothesis. This practice is acceptable because we only make the decision to reject or not reject the null hypothesis.

Example \(\PageIndex{1}\)

• \(H_{0}\): No more than 30% of the registered voters in Santa Clara County voted in the primary election. \(p \leq 30\)
• \(H_{a}\): More than 30% of the registered voters in Santa Clara County voted in the primary election. \(p > 30\)

Exercise \(\PageIndex{1}\)

A medical trial is conducted to test whether or not a new medicine reduces cholesterol by 25%. State the null and alternative hypotheses.

• \(H_{0}\): The drug reduces cholesterol by 25%. \(p = 0.25\)
• \(H_{a}\): The drug does not reduce cholesterol by 25%. \(p \neq 0.25\)

Example \(\PageIndex{2}\)

We want to test whether the mean GPA of students in American colleges is different from 2.0 (out of 4.0). The null and alternative hypotheses are:

• \(H_{0}: \mu = 2.0\)
• \(H_{a}: \mu \neq 2.0\)

Exercise \(\PageIndex{2}\)

We want to test whether the mean height of eighth graders is 66 inches. State the null and alternative hypotheses. Fill in the correct symbol \((=, \neq, \geq, <, \leq, >)\) for the null and alternative hypotheses.

• \(H_{0}: \mu \_ 66\)
• \(H_{a}: \mu \_ 66\)
• \(H_{0}: \mu = 66\)
• \(H_{a}: \mu \neq 66\)

Example \(\PageIndex{3}\)

We want to test if college students take less than five years to graduate from college, on the average. The null and alternative hypotheses are:

• \(H_{0}: \mu \geq 5\)
• \(H_{a}: \mu < 5\)

Exercise \(\PageIndex{3}\)

We want to test if it takes fewer than 45 minutes to teach a lesson plan. State the null and alternative hypotheses. Fill in the correct symbol ( =, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.

• \(H_{0}: \mu \_ 45\)
• \(H_{a}: \mu \_ 45\)
• \(H_{0}: \mu \geq 45\)
• \(H_{a}: \mu < 45\)

Example \(\PageIndex{4}\)

In an issue of U. S. News and World Report , an article on school standards stated that about half of all students in France, Germany, and Israel take advanced placement exams and a third pass. The same article stated that 6.6% of U.S. students take advanced placement exams and 4.4% pass. Test if the percentage of U.S. students who take advanced placement exams is more than 6.6%. State the null and alternative hypotheses.

• \(H_{0}: p \leq 0.066\)
• \(H_{a}: p > 0.066\)

Exercise \(\PageIndex{4}\)

On a state driver’s test, about 40% pass the test on the first try. We want to test if more than 40% pass on the first try. Fill in the correct symbol (\(=, \neq, \geq, <, \leq, >\)) for the null and alternative hypotheses.

• \(H_{0}: p \_ 0.40\)
• \(H_{a}: p \_ 0.40\)
• \(H_{0}: p = 0.40\)
• \(H_{a}: p > 0.40\)

COLLABORATIVE EXERCISE

Bring to class a newspaper, some news magazines, and some Internet articles . In groups, find articles from which your group can write null and alternative hypotheses. Discuss your hypotheses with the rest of the class.

In a hypothesis test , sample data is evaluated in order to arrive at a decision about some type of claim. If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. In a hypothesis test, we:

• Evaluate the null hypothesis , typically denoted with \(H_{0}\). The null is not rejected unless the hypothesis test shows otherwise. The null statement must always contain some form of equality \((=, \leq \text{or} \geq)\)
• Always write the alternative hypothesis , typically denoted with \(H_{a}\) or \(H_{1}\), using less than, greater than, or not equals symbols, i.e., \((\neq, >, \text{or} <)\).
• If we reject the null hypothesis, then we can assume there is enough evidence to support the alternative hypothesis.
• Never state that a claim is proven true or false. Keep in mind the underlying fact that hypothesis testing is based on probability laws; therefore, we can talk only in terms of non-absolute certainties.

Formula Review

\(H_{0}\) and \(H_{a}\) are contradictory.

• If \(\alpha \leq p\)-value, then do not reject \(H_{0}\).
• If\(\alpha > p\)-value, then reject \(H_{0}\).

\(\alpha\) is preconceived. Its value is set before the hypothesis test starts. The \(p\)-value is calculated from the data.References

Data from the National Institute of Mental Health. Available online at http://www.nimh.nih.gov/publicat/depression.cfm .

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AP Biology : Perform chi squared test

Study concepts, example questions & explanations for ap biology, all ap biology resources, example questions, example question #1 : perform chi squared test.

In a population of flowers, red is dominant to white. A true-breeding white flower is crossed with a heterozygous flower. Determine the expected ratios of this cross. Given observed values: 63 white flowers, 37 red flowers, determine:

1) The chi squared value

2) The degrees of freedom

3) The p-value

1) 6.76    

3) p>0.025

1) 14.44    

3) p>0.005

1) 3.35    

In a chi squared test, the greater the difference between the observed and expected frequencies of a trait, the ___ the p-value.

more positive

more negative

 A larger difference between observed and expected values will result in a larger chi square value. Using the critical values table, for a given degrees of freedom, as chi square value increases, p-value decreases.

Example Question #3 : Perform Chi Squared Test

If the p-value determined by a chi-square test is low, which is possible

The null hypothesis is rejected

The null hypothesis is accepted

The chi square value is also low

The alternative hypothesis is rejected

When the p-value is equal to or below the significance level (alpha), the null hypothesis is rejected. For the p-value to be low, the chi square value would need to be large (large difference between observed and expected values). The null hypothesis would not be accepted (under no circumstance is a null hypothesis “accepted”; onlay rejected or failed to reject). The alternative hypothesis would be accepted if the p-value is equal to or below the significance level (alpha).

Which of the following chi square values would likely result in rejection of the null hypothesis?

A larger difference between observed and expected values will result in a larger chi square value. Using the critical values table, for a given degrees of freedom, as chi square value increases, p-value decreases. A small p-value results in rejection of the null hypothesis.

Which is not a possible critical value of a chi square test?

Example Question #2 : Perform Chi Squared Test

If the chi square value is larger than the critical value at a given level of significance, what can be stated?

The null hypothesis fails to be rejected

If the chi square value is large, this indicates a large difference between the observed and expected values. This will subsequently result in a small p-value when using the critical value table. When the p-value is equal to or smaller than the significance level (alpha), the null hypothesis is rejected.

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Null hypothesis

Null hypothesis n., plural: null hypotheses [nʌl haɪˈpɒθɪsɪs] Definition: a hypothesis that is valid or presumed true until invalidated by a statistical test

Table of Contents

Null Hypothesis Definition

Null hypothesis is defined as “the commonly accepted fact (such as the sky is blue) and researcher aim to reject or nullify this fact”.

More formally, we can define a null hypothesis as “a statistical theory suggesting that no statistical relationship exists between given observed variables” .

In biology , the null hypothesis is used to nullify or reject a common belief. The researcher carries out the research which is aimed at rejecting the commonly accepted belief.

What Is a Null Hypothesis?

A hypothesis is defined as a theory or an assumption that is based on inadequate evidence. It needs and requires more experiments and testing for confirmation. There are two possibilities that by doing more experiments and testing, a hypothesis can be false or true. It means it can either prove wrong or true (Blackwelder, 1982).

For example, Susie assumes that mineral water helps in the better growth and nourishment of plants over distilled water. To prove this hypothesis, she performs this experiment for almost a month. She watered some plants with mineral water and some with distilled water.

In a hypothesis when there are no statistically significant relationships among the two variables, the hypothesis is said to be a null hypothesis. The investigator is trying to disprove such a hypothesis. In the above example of plants, the null hypothesis is:

There are no statistical relationships among the forms of water that are given to plants for growth and nourishment.

Usually, an investigator tries to prove the null hypothesis wrong and tries to explain a relation and association between the two variables.

An opposite and reverse of the null hypothesis are known as the alternate hypothesis . In the example of plants the alternate hypothesis is:

There are statistical relationships among the forms of water that are given to plants for growth and nourishment.

The example below shows the difference between null vs alternative hypotheses:

Alternate Hypothesis: The world is round Null Hypothesis: The world is not round.

Copernicus and many other scientists try to prove the null hypothesis wrong and false. By their experiments and testing, they make people believe that alternate hypotheses are correct and true. If they do not prove the null hypothesis experimentally wrong then people will not believe them and never consider the alternative hypothesis true and correct.

The alternative and null hypothesis for Susie’s assumption is:

• Null Hypothesis: If one plant is watered with distilled water and the other with mineral water, then there is no difference in the growth and nourishment of these two plants.
• Alternative Hypothesis:  If one plant is watered with distilled water and the other with mineral water, then the plant with mineral water shows better growth and nourishment.

The null hypothesis suggests that there is no significant or statistical relationship. The relation can either be in a single set of variables or among two sets of variables.

Most people consider the null hypothesis true and correct. Scientists work and perform different experiments and do a variety of research so that they can prove the null hypothesis wrong or nullify it. For this purpose, they design an alternate hypothesis that they think is correct or true. The null hypothesis symbol is H 0 (it is read as H null or H zero ).

Why is it named the “Null”?

The name null is given to this hypothesis to clarify and explain that the scientists are working to prove it false i.e. to nullify the hypothesis. Sometimes it confuses the readers; they might misunderstand it and think that statement has nothing. It is blank but, actually, it is not. It is more appropriate and suitable to call it a nullifiable hypothesis instead of the null hypothesis.

Why do we need to assess it? Why not just verify an alternate one?

In science, the scientific method is used. It involves a series of different steps. Scientists perform these steps so that a hypothesis can be proved false or true. Scientists do this to confirm that there will be any limitation or inadequacy in the new hypothesis. Experiments are done by considering both alternative and null hypotheses, which makes the research safe. It gives a negative as well as a bad impact on research if a null hypothesis is not included or a part of the study. It seems like you are not taking your research seriously and not concerned about it and just want to impose your results as correct and true if the null hypothesis is not a part of the study.

Development of the Null

In statistics, firstly it is necessary to design alternate and null hypotheses from the given problem. Splitting the problem into small steps makes the pathway towards the solution easier and less challenging. how to write a null hypothesis?

Writing a null hypothesis consists of two steps:

• Firstly, initiate by asking a question.
• Secondly, restate the question in such a way that it seems there are no relationships among the variables.

In other words, assume in such a way that the treatment does not have any effect.

The usual recovery duration after knee surgery is considered almost 8 weeks.

A researcher thinks that the recovery period may get elongated if patients go to a physiotherapist for rehabilitation twice per week, instead of thrice per week, i.e. recovery duration reduces if the patient goes three times for rehabilitation instead of two times.

Step 1: Look for the problem in the hypothesis. The hypothesis either be a word or can be a statement. In the above example the hypothesis is:

“The expected recovery period in knee rehabilitation is more than 8 weeks”

Step 2: Make a mathematical statement from the hypothesis. Averages can also be represented as μ, thus the null hypothesis formula will be.

In the above equation, the hypothesis is equivalent to H1, the average is denoted by μ and > that the average is greater than eight.

Step 3: Explain what will come up if the hypothesis does not come right i.e., the rehabilitation period may not proceed more than 08 weeks.

There are two options: either the recovery will be less than or equal to 8 weeks.

H 0 : μ ≤ 8

In the above equation, the null hypothesis is equivalent to H 0 , the average is denoted by μ and ≤ represents that the average is less than or equal to eight.

What will happen if the scientist does not have any knowledge about the outcome?

Problem: An investigator investigates the post-operative impact and influence of radical exercise on patients who have operative procedures of the knee. The chances are either the exercise will improve the recovery or will make it worse. The usual time for recovery is 8 weeks.

Step 1: Make a null hypothesis i.e. the exercise does not show any effect and the recovery time remains almost 8 weeks.

H 0 : μ = 8

In the above equation, the null hypothesis is equivalent to H 0 , the average is denoted by μ, and the equal sign (=) shows that the average is equal to eight.

Step 2: Make the alternate hypothesis which is the reverse of the null hypothesis. Particularly what will happen if treatment (exercise) makes an impact?

In the above equation, the alternate hypothesis is equivalent to H1, the average is denoted by μ and not equal sign (≠) represents that the average is not equal to eight.

Significance Tests

To get a reasonable and probable clarification of statistics (data), a significance test is performed. The null hypothesis does not have data. It is a piece of information or statement which contains numerical figures about the population. The data can be in different forms like in means or proportions. It can either be the difference of proportions and means or any odd ratio.

The following table will explain the symbols:

P-value is the chief statistical final result of the significance test of the null hypothesis.

• P-value = Pr(data or data more extreme | H 0 true)
• | = “given”
• Pr = probability
• H 0 = the null hypothesis

The first stage of Null Hypothesis Significance Testing (NHST) is to form an alternate and null hypothesis. By this, the research question can be briefly explained.

Null Hypothesis = no effect of treatment, no difference, no association Alternative Hypothesis = effective treatment, difference, association

When to reject the null hypothesis?

Researchers will reject the null hypothesis if it is proven wrong after experimentation. Researchers accept null hypothesis to be true and correct until it is proven wrong or false. On the other hand, the researchers try to strengthen the alternate hypothesis. The binomial test is performed on a sample and after that, a series of tests were performed (Frick, 1995).

Step 1: Evaluate and read the research question carefully and consciously and make a null hypothesis. Verify the sample that supports the binomial proportion. If there is no difference then find out the value of the binomial parameter.

Show the null hypothesis as:

H 0 :p= the value of p if H 0 is true

To find out how much it varies from the proposed data and the value of the null hypothesis, calculate the sample proportion.

Step 2: In test statistics, find the binomial test that comes under the null hypothesis. The test must be based on precise and thorough probabilities. Also make a list of pmf that apply, when the null hypothesis proves true and correct.

When H 0 is true, X~b(n, p)

N = size of the sample

P = assume value if H 0 proves true.

Step 3: Find out the value of P. P-value is the probability of data that is under observation.

Rise or increase in the P value = Pr(X ≥ x)

X = observed number of successes

P value = Pr(X ≤ x).

Step 4: Demonstrate the findings or outcomes in a descriptive detailed way.

• Sample proportion
• The direction of difference (either increases or decreases)

Perceived Problems With the Null Hypothesis

Variable or model selection and less information in some cases are the chief important issues that affect the testing of the null hypothesis. Statistical tests of the null hypothesis are reasonably not strong. There is randomization about significance. (Gill, 1999) The main issue with the testing of the null hypothesis is that they all are wrong or false on a ground basis.

There is another problem with the a-level . This is an ignored but also a well-known problem. The value of a-level is without a theoretical basis and thus there is randomization in conventional values, most commonly 0.q, 0.5, or 0.01. If a fixed value of a is used, it will result in the formation of two categories (significant and non-significant) The issue of a randomized rejection or non-rejection is also present when there is a practical matter which is the strong point of the evidence related to a scientific matter.

The P-value has the foremost importance in the testing of null hypothesis but as an inferential tool and for interpretation, it has a problem. The P-value is the probability of getting a test statistic at least as extreme as the observed one.

The main point about the definition is: Observed results are not based on a-value

Moreover, the evidence against the null hypothesis was overstated due to unobserved results. A-value has importance more than just being a statement. It is a precise statement about the evidence from the observed results or data. Similarly, researchers found that P-values are objectionable. They do not prefer null hypotheses in testing. It is also clear that the P-value is strictly dependent on the null hypothesis. It is computer-based statistics. In some precise experiments, the null hypothesis statistics and actual sampling distribution are closely related but this does not become possible in observational studies.

Some researchers pointed out that the P-value is depending on the sample size. If the true and exact difference is small, a null hypothesis even of a large sample may get rejected. This shows the difference between biological importance and statistical significance. (Killeen, 2005)

Another issue is the fix a-level, i.e., 0.1. On the basis, if a-level a null hypothesis of a large sample may get accepted or rejected. If the size of simple is infinity and the null hypothesis is proved true there are still chances of Type I error. That is the reason this approach or method is not considered consistent and reliable. There is also another problem that the exact information about the precision and size of the estimated effect cannot be known. The only solution is to state the size of the effect and its precision.

Null Hypothesis Examples

Here are some examples:

Example 1: Hypotheses with One Sample of One Categorical Variable

Among all the population of humans, almost 10% of people prefer to do their task with their left hand i.e. left-handed. Let suppose, a researcher in the Penn States says that the population of students at the College of Arts and Architecture is mostly left-handed as compared to the general population of humans in general public society. In this case, there is only a sample and there is a comparison among the known population values to the population proportion of sample value.

• Research Question: Do artists more expected to be left-handed as compared to the common population persons in society?
• Response Variable: Sorting the student into two categories. One category has left-handed persons and the other category have right-handed persons.
• Form Null Hypothesis: Arts and Architecture college students are no more predicted to be lefty as compared to the common population persons in society (Lefty students of Arts and Architecture college population is 10% or p= 0.10)

Example 2: Hypotheses with One Sample of One Measurement Variable

A generic brand of antihistamine Diphenhydramine making medicine in the form of a capsule, having a 50mg dose. The maker of the medicines is concerned that the machine has come out of calibration and is not making more capsules with the suitable and appropriate dose.

• Research Question: Does the statistical data recommended about the mean and average dosage of the population differ from 50mg?
• Response Variable: Chemical assay used to find the appropriate dosage of the active ingredient.
• Null Hypothesis: Usually, the 50mg dosage of capsules of this trade name (population average and means dosage =50 mg).

Example 3: Hypotheses with Two Samples of One Categorical Variable

Several people choose vegetarian meals on a daily basis. Typically, the researcher thought that females like vegetarian meals more than males.

• Research Question: Does the data recommend that females (women) prefer vegetarian meals more than males (men) regularly?
• Response Variable: Cataloguing the persons into vegetarian and non-vegetarian categories. Grouping Variable: Gender
• Null Hypothesis: Gender is not linked to those who like vegetarian meals. (Population percent of women who eat vegetarian meals regularly = population percent of men who eat vegetarian meals regularly or p women = p men).

Example 4: Hypotheses with Two Samples of One Measurement Variable

Nowadays obesity and being overweight is one of the major and dangerous health issues. Research is performed to confirm that a low carbohydrates diet leads to faster weight loss than a low-fat diet.

• Research Question: Does the given data recommend that usually, a low-carbohydrate diet helps in losing weight faster as compared to a low-fat diet?
• Response Variable: Weight loss (pounds)
• Explanatory Variable: Form of diet either low carbohydrate or low fat
• Null Hypothesis: There is no significant difference when comparing the mean loss of weight of people using a low carbohydrate diet to people using a diet having low fat. (population means loss of weight on a low carbohydrate diet = population means loss of weight on a diet containing low fat).

Example 5: Hypotheses about the relationship between Two Categorical Variables

A case-control study was performed. The study contains nonsmokers, stroke patients, and controls. The subjects are of the same occupation and age and the question was asked if someone at their home or close surrounding smokes?

• Research Question: Did second-hand smoke enhance the chances of stroke?
• Variables: There are 02 diverse categories of variables. (Controls and stroke patients) (whether the smoker lives in the same house). The chances of having a stroke will be increased if a person is living with a smoker.
• Null Hypothesis: There is no significant relationship between a passive smoker and stroke or brain attack. (odds ratio between stroke and the passive smoker is equal to 1).

Example 6: Hypotheses about the relationship between Two Measurement Variables

A financial expert observes that there is somehow a positive and effective relationship between the variation in stock rate price and the quantity of stock bought by non-management employees

• Response variable- Regular alteration in price
• Explanatory Variable- Stock bought by non-management employees
• Null Hypothesis: The association and relationship between the regular stock price alteration ($) and the daily stock-buying by non-management employees ($) = 0.

Example 7: Hypotheses about comparing the relationship between Two Measurement Variables in Two Samples

• Research Question: Is the relation between the bill paid in a restaurant and the tip given to the waiter, is linear? Is this relation different for dining and family restaurants?
• Explanatory Variable- total bill amount
• Response Variable- the amount of tip
• Null Hypothesis: The relationship and association between the total bill quantity at a family or dining restaurant and the tip, is the same.

Try to answer the quiz below to check what you have learned so far about the null hypothesis.

Choose the best answer. 

Send Your Results (Optional)

• Blackwelder, W. C. (1982). “Proving the null hypothesis” in clinical trials. Controlled Clinical Trials , 3(4), 345–353.
• Frick, R. W. (1995). Accepting the null hypothesis. Memory & Cognition, 23(1), 132–138.
• Gill, J. (1999). The insignificance of null hypothesis significance testing. Political Research Quarterly , 52(3), 647–674.
• Killeen, P. R. (2005). An alternative to null-hypothesis significance tests. Psychological Science, 16(5), 345–353.

©BiologyOnline.com. Content provided and moderated by Biology Online Editors.

Last updated on June 16th, 2022

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20.3 Perspectives on the Phylogenetic Tree

In this section, you will explore the following questions:

• What is horizontal gene transfer and its significance in constructing phylogenetic trees?
• How do prokaryotes and eukaryotes transfer genes horizontally?
• What are other models of phylogenetic relationships and how do they differ from the original phylogenetic tree concept?

Connection for AP ® Courses

Newer technologies have uncovered surprising discoveries with unexpected relationships among organisms, such as the fact that humans seems to be more closely related to fungi than fungi are to plants. (Think about that the next time you see a mushroom). As the information about DNA sequences grows, scientists will become closer to mapping a more accurate evolutionary history of all life on Earth.

What makes phylogeny difficult, especially among prokaryotes, is the transfer of genes horizontally ( horizontal gene transfer , or HGT ) between unrelated species. Like mutations, HGT introduces genetic variation into the bacterial population. This passing of genes between species adds a layer of complexity to understanding relatedness.

Information presented and the examples highlighted in the section support concepts outlined in Big Idea 3 of the AP ® Biology Curriculum Framework. The AP ® Learning Objectives listed in the Curriculum Framework provide a transparent foundation for the AP ® Biology course, an inquiry-based laboratory experience, instructional activities, and AP ® exam questions. A learning objective merges required content with one or more of the seven science practices.

The concepts of phylogenetic modeling are constantly changing. It is one of the most dynamic fields of study in all of biology. Over the last several decades, new research has challenged scientists’ ideas about how organisms are related. New models of these relationships have been proposed for consideration by the scientific community.

Many phylogenetic trees have been shown as models of the evolutionary relationship among species. Phylogenetic trees originated with Charles Darwin, who sketched the first phylogenetic tree in 1837 ( Figure 20.12 a ), which served as a pattern for subsequent studies for more than a century. he phylogenetic tree concept with a single trunk representing a shared ancestry, with the branches representing the divergence of species from this ancestry, fits well with the structure of many common trees, such as the oak ( Figure 20.12 b ). However, evidence from modern DNA sequence analysis and newly developed computer algorithms has caused skepticism about the validity of the standard tree model in the scientific community.

Limitations to the Classic Model

Classical thinking about prokaryotic evolution, included in the classic tree model, is that species evolve clonally. That is, they produce offspring themselves with only random mutations causing the descent into the variety of modern-day and extinct species known to science. This view is somewhat complicated in eukaryotes that reproduce sexually, but the laws of Mendelian genetics explain the variation in offspring, again, to be a result of a mutation within the species. The concept of genes being transferred between unrelated species was not considered as a possibility until relatively recently. Horizontal gene transfer (HGT), also known as lateral gene transfer, is the transfer of genes between unrelated species. HGT has been shown to be an ever-present phenomenon, with many evolutionists postulating a major role for this process in evolution, thus complicating the simple tree model. Genes have been shown to be passed between species which are only distantly related using standard phylogeny, thus adding a layer of complexity to the understanding of phylogenetic relationships.

The various ways that HGT occurs in prokaryotes is important to understanding phylogenies. Although at present HGT is not viewed as important to eukaryotic evolution, HGT does occur in this domain as well. Finally, as an example of the ultimate gene transfer, theories of genome fusion between symbiotic or endosymbiotic organisms have been proposed to explain an event of great importance—the evolution of the first eukaryotic cell, without which humans could not have come into existence.

Horizontal Gene Transfer

Horizontal gene transfer (HGT) is the introduction of genetic material from one species to another species by mechanisms other than the vertical transmission from parent(s) to offspring. These transfers allow even distantly related species to share genes, influencing their phenotypes. It is thought that HGT is more prevalent in prokaryotes, but that only about 2% of the prokaryotic genome may be transferred by this process. Some researchers believe such estimates are premature: the actual importance of HGT to evolutionary processes must be viewed as a work in progress. As the phenomenon is investigated more thoroughly, it may be revealed to be more common. Many scientists believe that HGT and mutation appear to be (especially in prokaryotes) a significant source of genetic variation, which is the raw material for the process of natural selection. These transfers may occur between any two species that share an intimate relationship ( Table 20.1 ).

HGT in Prokaryotes

The mechanism of HGT has been shown to be quite common in the prokaryotic domains of Bacteria and Archaea, significantly changing the way their evolution is viewed. The majority of evolutionary models, such as in the Endosymbiont Theory, propose that eukaryotes descended from multiple prokaryotes, which makes HGT all the more important to understanding the phylogenetic relationships of all extant and extinct species.

The fact that genes are transferred among common bacteria is well known to microbiology students. These gene transfers between species are the major mechanism whereby bacteria acquire resistance to antibiotics. Classically, this type of transfer has been thought to occur by three different mechanisms:

• Transformation: naked DNA is taken up by a bacteria
• Transduction: genes are transferred using a virus
• Conjugation: the use a hollow tube called a pilus to transfer genes between organisms

More recently, a fourth mechanism of gene transfer between prokaryotes has been discovered. Small, virus-like particles called gene transfer agents (GTAs) transfer random genomic segments from one species of prokaryote to another. GTAs have been shown to be responsible for genetic changes, sometimes at a very high frequency compared to other evolutionary processes. The first GTA was characterized in 1974 using purple, non-sulfur bacteria. These GTAs, which are thought to be bacteriophages that lost the ability to reproduce on their own, carry random pieces of DNA from one organism to another. The ability of GTAs to act with high frequency has been demonstrated in controlled studies using marine bacteria. Gene transfer events in marine prokaryotes, either by GTAs or by viruses, have been estimated to be as high as 10 13 per year in the Mediterranean Sea alone. GTAs and viruses are thought to be efficient HGT vehicles with a major impact on prokaryotic evolution.

As a consequence of this modern DNA analysis, the idea that eukaryotes evolved directly from Archaea has fallen out of favor. While eukaryotes share many features that are absent in bacteria, such as the TATA box (found in the promoter region of many genes), the discovery that some eukaryotic genes were more homologous with bacterial DNA than Archaea DNA made this idea less tenable. Furthermore, the fusion of genomes from Archaea and Bacteria by endosymbiosis has been proposed as the ultimate event in eukaryotic evolution.

HGT in Eukaryotes

Although it is easy to see how prokaryotes exchange genetic material by HGT, it was initially thought that this process was absent in eukaryotes. After all, prokaryotes are but single cells exposed directly to their environment, whereas the sex cells of multicellular organisms are usually sequestered in protected parts of the body. It follows from this idea that the gene transfers between multicellular eukaryotes should be more difficult. Indeed, it is thought that this process is rarer in eukaryotes and has a much smaller evolutionary impact than in prokaryotes. In spite of this fact, HGT between distantly related organisms has been demonstrated in several eukaryotic species, and it is possible that more examples will be discovered in the future.

In plants, gene transfer has been observed in species that cannot cross-pollinate by normal means. Transposons or “jumping genes” have been shown to transfer between rice and millet plant species. Furthermore, fungal species feeding on yew trees, from which the anti-cancer drug paclitaxel is derived from the bark, have acquired the ability to make paclitaxel themselves, a clear example of gene transfer.

In animals, a particularly interesting example of HGT occurs within the aphid species ( Figure 20.13 ). Aphids are insects that vary in color based on carotenoid content. Carotenoids are pigments made by a variety of plants, fungi, and microbes, and they serve a variety of functions in animals, who obtain these chemicals from their food. Humans require carotenoids to synthesize vitamin A, and we obtain them by eating orange fruits and vegetables: carrots, apricots, mangoes, and sweet potatoes. On the other hand, aphids have acquired the ability to make the carotenoids on their own. According to DNA analysis, this ability is due to the transfer of fungal genes into the insect by HGT, presumably as the insect consumed fungi for food. A carotenoid enzyme called a desaturase is responsible for the red coloration seen in certain aphids, and it has been further shown that when this gene is mutated and the enzyme looses activity, the aphids revert back to their more common green color ( Figure 20.13 ).

Everyday Connection for AP® Courses

Barbara McClintock (1902–1992) discovered transposons while working on maize genetics.

• that mitochondria were first established in a prokaryotic host which acquired a nucleus to become the first eukaryotic cell
• that the nucleus evolved in prokaryotes first followed by fusion of the new eukaryote with bacteria that became mitochondria
• that prokaryotes actually evolved from eukaryotes by losing genes and complexity
• that eukaryotes developed Golgi before mitochondria

Genome Fusion and the Evolution of Eukaryotes

Scientists believe the ultimate in HGT occurs through genome fusion between different species of prokaryotes when two symbiotic organisms become endosymbiotic. This occurs when one species is taken inside the cytoplasm of another species, which ultimately results in a genome consisting of genes from both the endosymbiont and the host. This mechanism is an aspect of the Endosymbiont Theory, which is accepted by a majority of biologists as the mechanism whereby eukaryotic cells obtained their mitochondria and chloroplasts. However, the role of endosymbiosis in the development of the nucleus is more controversial. Nuclear and mitochondrial DNA are thought to be of different (separate) evolutionary origin, with the mitochondrial DNA being derived from the circular genomes of bacteria that were engulfed by ancient prokaryotic cells. Mitochondrial DNA can be regarded as the smallest chromosome. Interestingly enough, mitochondrial DNA is inherited only from the mother. The mitochondrial DNA degrades in sperm when the sperm degrades in the fertilized egg or in other instances when the mitochondria located in the flagellum of the sperm fails to enter the egg.

Within the past decade, the process of genome fusion by endosymbiosis has been proposed by James Lake of the UCLA/NASA Astrobiology Institute to be responsible for the evolution of the first eukaryotic cells ( Figure 20.15 a ). Using DNA analysis and a new mathematical algorithm called conditioned reconstruction (CR), his laboratory proposed that eukaryotic cells developed from an endosymbiotic gene fusion between two species, one an Archaea and the other a Bacteria. As mentioned, some eukaryotic genes resemble those of Archaea, whereas others resemble those from Bacteria. An endosymbiotic fusion event, such as Lake has proposed, would clearly explain this observation. On the other hand, this work is new and the CR algorithm is relatively unsubstantiated, which causes many scientists to resist this hypothesis.

More recent work by Lake ( Figure 20.15 b ) proposes that gram-negative bacteria, which are unique within their domain in that they contain two lipid bilayer membranes, indeed resulted from an endosymbiotic fusion of archaeal and bacterial species. The double membrane would be a direct result of the endosymbiosis, with the endosymbiont picking up the second membrane from the host as it was internalized. This mechanism has also been used to explain the double membranes found in mitochondria and chloroplasts. Some are skeptical of Lake’s work, and the biological science community still debates his ideas. In addition to Lake’s hypothesis, there are several other competing theories as to the origin of eukaryotes. How did the eukaryotic nucleus evolve? One theory is that the prokaryotic cells produced an additional membrane that surrounded the bacterial chromosome. Some bacteria have the DNA enclosed by two membranes; however, there is no evidence of a nucleolus or nuclear pores. Other proteobacteria also have membrane-bound chromosomes. If the eukaryotic nucleus evolved this way, we would expect one of the two types of prokaryotes to be more closely related to eukaryotes.

The nucleus-first hypothesis proposes that the nucleus evolved in prokaryotes first ( Figure 20.16 a ), followed by a later fusion of the new eukaryote with bacteria that became mitochondria. The mitochondria-first hypothesis proposes that mitochondria were first established in a prokaryotic host ( Figure 20.16 b ), which subsequently acquired a nucleus, by fusion or other mechanisms, to become the first eukaryotic cell. Most interestingly, the eukaryote-first hypothesis proposes that prokaryotes actually evolved from eukaryotes by losing genes and complexity ( Figure 20.16 c ). All of these hypotheses are testable. Only time and more experimentation will determine which hypothesis is best supported by data.

Web and Network Models

The recognition of the importance of HGT, especially in the evolution of prokaryotes, has caused some to propose abandoning the classic “tree of life” model. In 1999, W. Ford Doolittle proposed a phylogenetic model that resembles a web or a network more than a tree. The hypothesis is that eukaryotes evolved not from a single prokaryotic ancestor, but from a pool of many species that were sharing genes by HGT mechanisms. As shown in Figure 20.17 a , some individual prokaryotes were responsible for transferring the bacteria that caused mitochondrial development to the new eukaryotes, whereas other species transferred the bacteria that gave rise to chloroplasts. This model is often called the “ web of life .” In an effort to save the tree analogy, some have proposed using the Ficus tree ( Figure 20.17 b ) with its multiple trunks as a phylogenetic to represent a diminished evolutionary role for HGT.

Ring of Life Models

Others have proposed abandoning any tree-like model of phylogeny in favor of a ring structure, the so-called “ ring of life ” ( Figure 20.18 ); a phylogenetic model where all three domains of life evolved from a pool of primitive prokaryotes. Lake, again using the conditioned reconstruction algorithm, proposes a ring-like model in which species of all three domains—Archaea, Bacteria, and Eukarya—evolved from a single pool of gene-swapping prokaryotes. His laboratory proposes that this structure is the best fit for data from extensive DNA analyses performed in his laboratory, and that the ring model is the only one that adequately takes HGT and genomic fusion into account. However, other phylogeneticists remain highly skeptical of this model.

In summary, the “tree of life” model proposed by Darwin must be modified to include HGT. Does this mean abandoning the tree model completely? Even Lake argues that all attempts should be made to discover some modification of the tree model to allow it to accurately fit his data, and only the inability to do so will sway people toward his ring proposal.

This doesn’t mean a tree, web, or a ring will correlate completely to an accurate description of phylogenetic relationships of life. A consequence of the new thinking about phylogenetic models is the idea that Darwin’s original conception of the phylogenetic tree is too simple, but made sense based on what was known at the time. However, the search for a more useful model moves on: each model serving as hypotheses to be tested with the possibility of developing new models. This is how science advances. These models are used as visualizations to help construct hypothetical evolutionary relationships and understand the massive amount of data being analyzed.

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Sat / act prep online guides and tips, what's in an ap biology syllabus guide and examples.

Advanced Placement (AP)

A great syllabus is the backbone of any AP course, but it can be challenging to create one that fits all the requirements and is easy for students to follow. In this article, I'll give you the ingredients you need for a solid AP Biology syllabus, including all the concepts and curricular requirements of the course.

I'll also provide a sample syllabus so you can get an idea of what a syllabus for this class might look like (if you're a student) or how you might structure your version of AP Biology (if you're a teacher). Finally, the end of this article is devoted to a few quick tips for teachers and students on how to successfully teach and learn the material.

What Does the AP Biology Course Cover?

The curriculum framework of AP Biology was revamped in 2012 and is now organized around four Big Ideas , which are overarching themes that connect the concepts you’ll learn in the course. 

Within each Big Idea are several “Enduring Understandings” that students are expected to develop by taking the course. These are slightly narrower themes that  can be broken down even further into smaller parts that are aligned with specific facts about biological functions.

I’ll list the Big Ideas and their corresponding Enduring Understandings in this article, along with the labs that fall under each category. I won’t go into the smaller concepts within the Enduring Understandings so that this guide doesn’t get too long to digest. If you’re interested in a detailed rundown of the more minute concepts involved in AP Biology,  check out this page .

As students explore the Big Ideas, they are also expected to learn several key scientific practices, and the classwork must cover certain curricular requirements.   After I go through the Big Ideas, I’ll list the scientific practices students should develop and the curricular requirements that any AP Biology syllabus is expected to fulfill.

The Four Big Ideas of AP Biology 

Big idea #1: the process of evolution drives the diversity and unity of life..

• Enduring Understanding 1.1: Change in the genetic makeup of a population over time is evolution.
• EU 1.2: Organisms are linked by lines of descent from common ancestry.
• EU 1.3: Life continues to evolve within a changing environment.
• EU 1.4: The origin of living systems is explained by natural processes.

Labs for Big Idea 1:

• Artificial selection
• Mathematical modeling (Hardy-Weinberg)
• Comparing DNA Sequences to Understand Evolutionary Relationships

Big Idea #2: Biological systems utilize free energy and molecular building blocks to grow, to reproduce and to maintain dynamic homeostasis.

• EU 2.1: Growth, reproduction and maintenance of living systems require free energy and matter.
• EU 2.2: Growth, reproduction and dynamic homeostasis require that cells maintain internal environments that are different from their external environments.
• EU 2.3: Organisms use feedback mechanisms to regulate growth and reproduction, and to maintain dynamic homeostasis.
• EU 2.4: Growth and dynamic homeostasis of a biological system are influenced by changes in the system’s environment.
• EU 2.5: Many biological processes involved in growth, reproduction and dynamic homeostasis include temporal regulation and coordination.

Labs for Big Idea 2:

• Diffusion and Osmosis
• Photosynthesis
• Cellular Respiration  

Big Idea #3: Living systems store, receive, transmit and respond to information essential to life processes

• EU 3.1: Heritable information provides for the continuity of life.
• EU 3.2: Expression of genetic information involves cellular and molecular mechanisms.
• EU 3.3: The processing of genetic information is imperfect and is a source of genetic variation.
• EU 3.4: Cells communicate by generating, transmitting and receiving chemical signals.
• EU 3.5: Transmission of information results in changes within and between biological systems.

Labs for Big Idea 3:

• Cell Division: Mitosis and Meiosis
• Biotechnology: Bacterial Transformation
• Biotechnology: Restriction Enzyme Analysis of DNA  

Big Idea #4: Biological systems interact, and these systems and their interactions possess complex properties

• EU 4.1: Interactions within biological systems lead to complex properties.
• EU 4.2: Competition and cooperation are important aspects of biological systems.
• EU 4.3: Naturally occurring diversity among and between components within biological systems affects interactions with the environment.

Labs for Big Idea 4:

• Energy Dynamics
• Transpiration
• Fruit Fly Behavior
• Enzyme Activity  

Cooperation is always happening in biological systems! This is an approximation of what the inside of a cell looks like at any given time.

The Seven Scientific Practices of AP Biology 

#1:  The student can use representations and models to communicate scientific phenomena and solve scientific problems.

#2:   The student can use mathematics appropriately.

#3:  The student can engage in scientific questioning to extend thinking or to guide investigations within the context of the AP course.

#4: The student can plan and implement data collection strategies in relation to a particular scientific question. (Note: Data can be collected from many different sources, e.g., investigations, scientific observations, the findings of others, historic reconstruction and/or archived data.)

#5: The student can perform data analysis and evaluation of evidence.

#6: The student can work with scientific explanations and theories.

#7: The student is able to connect and relate knowledge across various scales, concepts and representations in and across domains.

The Curricular Requirements of AP Biology 

Here's a list of the requirements that an AP Biology course must fulfill to be considered an appropriate and thorough survey of the material:

• The course must use a college-level biology textbook published within the last ten years.
• Students must connect the Enduring Understandings of each Big Idea to at least one other Big Idea (for example, connect the evolutionary concepts in Big Idea 1 to the idea presented under Big Idea 3 that genetic information is sometimes processed imperfectly, and this leads to variation).
• Students should be assigned projects and activities outside of labs to meet the learning objectives for each Big Idea (for example, students might do an activity where they create a model of the cell cycle and give an oral presentation on its most important aspects).
• The course must give students the opportunity to connect biological knowledge to major social issues and current events (for example, a project researching stem cells and their potential to impact the medical field).
• Labs must give students the opportunity to apply the seven science practices I listed earlier, and the course has to go through at least two labs that correspond with each Big Idea.
• Labs must make up at least 25% of class time.
• Students should be asked to demonstrate verbal, written, and visual communication skills with lab reports, summaries of scientific literature or evidence, and oral presentations.

What Does an AP Biology Syllabus Look Like?

The College Board has released some sample syllabi to help guide teachers in their instruction of the new format of the AP Biology course.  The example that I’m looking at divides the class into nine different units of varying length. 

The resources for the course include:

• Reece, Jane, et al., Campbell Biology , 9th Edition, 2011, Pearson Benjamin Cummings
• www.campbellbiology.com  (The main text’s accompanying website that provides animations, investigations, PowerPoint and other audio-visual resources)
• Giffen, Cynthia and Heitz, Jean. Practicing Biology (to accompany Campbell- Reece Biology), 3rd Edition, 2008, Pearson Benjamin Cummings
• AP Biology Investigative Labs: an Inquiry Based Approach

I’ll give an overview of the topics discussed in each unit and the projects and labs students might complete throughout the year.   I’ll also provide the approximate length of each unit.  In this case, the class met four times a week for two 80-minute periods and two 50-minute periods (4 hours and 20 minutes a week total).

Sample AP Biology Syllabus

Unit 1: first week and introduction (4 classes).

Textbook Chapters:

• Introduction: Themes in the Study of Life
• The Chemical Context of Life
• Water and the Fitness of the Environment

Lecture and Discussion Topics:

• Darwin and the theory of natural selection
• Inquiry as a way to learn science
• Structure of atoms
• Emergent properties of water
• Make construction paper models of atoms and molecules to illustrate chemical concepts.
• Conduct an open inquiry on a biological topic of choice; formulate a question, design an experiment, and present the findings.

Unit 2: Biochemistry and Introduction to the Cell (11 classes)

4. Carbon and the Molecular Diversity of Life 5. The Structure and Function of Large Biological Molecules 6. A Tour of the Cell 7. Membrane Structure and Function

• The impact of carbon as the “backbone of life”
• How monomers build polymers, including the roles of nucleic acids
• Examples of organelles that are membrane bound to compartmentalize their functions
• Membrane structure and function
• Build a 3D cell membrane
• Diffusion and Osmosis Lab

Unit 3: Cellular Energy and Related Processes (14 classes)

8. An Introduction to Metabolism 9. Cellular Respiration 10. Photosynthesis

• Metabolic pathways
• Laws of energy transformation
• How ATP powers cellular work
• Enzyme structure and function
• Harvesting chemical energy: glycolysis, citric acid cycle, oxidative phosphorylation
• Light reactions and the Calvin cycle
• Evolution of alternative mechanism of carbon fixation
• Cellular Respiration Lab
• Photosynthesis Lab
• Enzyme Catalysis Lab

Unit 4: Cell Communication and the Cell Structure (9 classes)

11. Cell Communication 12. The Cell Cycle

• Evolution of cell signaling
• Reception, transduction, response
• How mitosis produces genetically identical daughter cells
• Evolution of mitosis
• How the eukaryotic cell cycle is regulated by a molecular control system
• Origin of cell communication

Projects and Labs:

• Pathways with Friends
• Modeling the Cell Cycle
• Cell Division and Mitosis Lab

Unit 5: Genetic Basis of Life (7 classes)

Textbook Chapters

13. Meiosis and Sexual Life Cycles 14. Mendel and the Gene Idea 15. The Chromosomal Basis of Inheritance

• Genes are passed from parents to offspring by the inheritance of chromosomes
• How meiosis reduces the number of chromosomes (diploid to haploid)
• Evolutionary significance of genetic variation that results from sexual life cycles
• Concepts of Mendelian genetics (laws of probability, inheritance patterns)
• Genes are located along chromosomes (concepts of gene linkage, mapping distance between genes, causes of genetic disorders)

Projects and Labs

• Fruit Fly Genetics Lab
• Meiosis Lab

Unit 6: Gene Activity and Biotechnology (13 classes)

16. The Molecular Basis of Inheritance 17. From Gene to Protein 18. Regulation of Gene Expression 19. Viruses  20. Biotechnology  21. Genomes and their Evolution

• DNA is the genetic material (historical experiments, DNA structure and function, DNA replication)
• Flow of genetic information (genetic code, role of other polymers, transcription, translation)
• Gene expression (operon systems in prokaryotes, eukaryotic gene expression)
• Virus structure and activity
• Restriction enzymes, plasmids, transformation
• DNA technology (how gel electrophoresis works and applications of this technology)
• DNA and Histone Model
• Biotechnology Lab 1: Transformation
• Gel Electrophoresis Lab

Unit 7: Evolution and Phylogeny (19 classes)

22. Descent with Modification: A Darwinian View of Life 23. The Evolution of Populations 24. The Origin of Species 25. The History of Life on Earth 26. Phylogeny and the Tree of Life 27. Bacteria and Archae

• How natural selection serves as a mechanism for evolution
• Scientific evidence supporting evolution
• Hardy-Weinberg concept
• How allele frequencies can be altered in a population
• Concepts of speciation
• Origin of life; fossil records
• Events in the “history of life” (origin of single-celled and multicellular organisms; mass extinctions; adaptive radiations)
• Comparing DNA Sequences Lab
• PBS Video: “What Darwin Never Knew”
• Evolutionary Time: The Geologic Time String
• Hardy-Weinberg  Problems

Unit 8: Diversity in the Biological World: Organism Form and Function (22 classes)

40. Basic Principles of Animal Form and Function 43. The Immune System 48. Neurons, Synapses, and Signaling 49. The Vertebrate Brain (Chapters 28-49 will be utilized to provide students with resources for the enduring understandings in this unit)

• Evolutionary trends (endosymbiosis, adaptations that allowed plants to move from water to land, reproductive adaptations of angiosperms, environmental roles of fungi, animal body plans, progressively complex derived characters in animal groups)
• Unique features of the angiosperm life cycles
• Signal transduction pathways (plant and animal hormones)
• Photoperiodism in plants
• Feedback control loops in animals
• Thermoregulation in animals
• Energy allocation and use in animals
• Examples of functioning units in mammal systems (alveoli in lungs, villi of small intestines, nephrons in kidneys)
• Structure and function in immune systems
• Structure and function in nervous systems (neurons, resting potential, action potential, synapses)
• Structure and function of the human brain
• Construct a cladogram for a group of organisms with certain traits
• Jumpin’ the Gap (students pretend to be components of neural communication)
• Research project on stem cells and whether they should be used to treat brain and spinal cord injuries

Unit 9: Ecology (17 classes)

• Aspects of animal behavior
• Aspects of biomes
• Models describing population growth
• Regulation of population growth
• Community interactions
• Species diversity and composition
• Community biodiversity
• Energy flow and chemical cycling in ecosystems
• Primary productivity
• Energy transfer between trophic levels
• Human activities that threaten biodiversity
• Fruit Fly Behavior Lab
• Dissolved Oxygen and Primary Productivity Lab
• Design a model of a biome
• Improving species richness by adding phosphate to a pond - how would you determine how much to add to avoid eutrophication? Present your hypothesis.
• Investigate how the fungus Pilobolus succeeds as a decomposer? Study adaptiveness of spore dispersal methods

Teaching Tips

Writing a great syllabus is the first order of business, but as you probably know, most of teaching is in the execution. If you’re a teacher trying to provide the best experience for your AP Biology students, here are a few tips you might consider using in your lessons.

#1: Provide Brief Lecture Outlines

Give brief outlines to students before you start your lecture so that they’ll have a clearer picture of what you’re going to cover. I say brief outlines because you don’t want to give them notes that list everything they need to know about the lecture topic.  List the main points of the lecture (around 3-5 of them), and list a couple of important subtopics under each. Provide plenty of space between concepts for students to write notes. They  should have some incentive to pay attention in class.  This will create a better environment where students aren't confused or tuned out.  

#2: Break Up Your Lectures With Class Discussions

I remember dreading double-period lecture classes in high school, and I was better than most high school students at sitting quietly and forgoing social interaction.  You should try to engage the class in a discussion midway through your lecture to break up the monotony.   I’d recommend calling on people randomly so that kids will have an incentive to pay attention and more introverted students will get a chance to participate in the discussion.

#3: Be Accessible During Labs

Chances are, kids will have tons of questions during labs. Sometimes the procedure is a little confusing or the results are different from what was expected.  Make a point of walking around the room and checking in with each lab group to ensure that everyone stays on task and students have a chance to ask questions.  This can also prevent students from doing the lab incorrectly and wasting time - you’ll catch mistakes early!  

#4: Model Your Tests After the Real AP Test

My AP Biology teacher gave us tests throughout the year whose questions were very similar to real AP test questions.   These tests were super challenging (I don’t think I ever got a solid A on any of them), but I was very well-prepared for the AP test.  Nothing on the exam was more difficult than the questions I had encountered on in-class tests , so I felt pretty confident.  

Tips for Students

Here are a few additional tips directed towards students who want to do well in AP Biology.  

#1: Do the Readings on Time

There’s a lot to cover in this class, so it’s critical that you keep up with the readings in your textbook.  If you fall behind, it will be hard to catch up.   There also may be important things that your textbook covers that your teacher won’t mention in lectures.  Plus, you’ll be able to participate in class discussions and avoid failing any pop quizzes!

#2: Take Notes During Lectures

Don’t zone out when your teacher is talking!  I know it can be difficult, but taking effective notes is a great skill to have for college and beyond.  Your notes will also help you study for in-class tests and, eventually, the AP test. It’s easier to study your own notes because they’ll be written in a way that you understand.  

#3: Ask Questions

Don’t be afraid to speak up in class discussions and engage with the lecture topics.  If you feel like you don’t understand a concept, ask your teacher about it. B elieve me, it's better than finding yourself totally lost later! Also, if you have any doubts about lab procedures or how you should write your lab reports, consult with your teacher before going forward.

AP Biology covers a huge amount of information, so writing a syllabus that organizes everything effectively is super important. The four main Big Ideas encompass many smaller themes, each of which covers a variety of complex concepts.

The College Board also requires classes to introduce students to seven scientific practices and fulfill a litany of other curricular requirements. Hopefully, the sample syllabus in this article gave you a good idea of what the structure of an AP Biology class should look like and how you might choose to cover all of the material.  Guiding students through this intimidating maze of concepts can be pretty difficult!

To review, some teaching strategies I recommend are:

• Providing brief lecture outlines
• Breaking lectures into more manageable chunks
• Being available for questions during labs
• Using tests that mimic the structure and content of the AP test

For students, here are a few other pieces of advice that I would suggest following:

• Keep up with the readings 
• Take notes on lectures
• Ask lots of questions

What's Next?

Looking for some good AP Biology review books? Check out my guide to the best books for this year's version of the test.

I've also written a complete study guide for the AP Biology test that goes through all the concepts and has links to free online resources that you can use to review.

Finally, this article goes through an analysis of whether AP Biology is more or less difficult in comparison to other AP classes and tests. It might be helpful if you're not sure about taking the course or just want some insight into how much you'll need to study for the test!

Samantha is a blog content writer for PrepScholar. Her goal is to help students adopt a less stressful view of standardized testing and other academic challenges through her articles. Samantha is also passionate about art and graduated with honors from Dartmouth College as a Studio Art major in 2014. In high school, she earned a 2400 on the SAT, 5's on all seven of her AP tests, and was named a National Merit Scholar.

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