Updated: Sep 22
On the AP Biology exam, the first section is multiple-choice and the second section is a set of 8 FRQs (free response questions), in which you may be given an experiment setup or asked to design an experiment yourself. Many students find the FRQs challenging because experimental design is not a specific chapter in the AP Biology textbook.
In order to answer these questions well, you need to put on your scientist’s hat and think about it as if you were running the experiment. The best way to demonstrate this is to walk through some examples of experiments. First, we will discuss the guidelines and terminology used for designing and running experiments in biology.
An experiment should always be based on a hypothesis, something that you believe might be true and that you want to test. If there is no hypothesis, there is no purpose for the experiment. Often, the hypothesis is an association between a factor and a result of interest. Some examples are:
Sunlight and plant growth
Mutation in bacteria and resistance to an antibiotic
A particular drug and decreased blood pressure
Soil acidity and flower color
Let’s take the second example, a particular mutation in bacteria and resistance to a specific antibiotic. There are so many different aspects of bacteria and the environment they live in. How can we determine that one particular trait (in this case, a mutated gene) is responsible for antibiotic resistance?
This is why scientists use controlled environments for their experiments. They can control for all factors ( keep them the same) across all experimental groups except the suspected factor, the gene mutation. Each experimental group has a different treatment or condition. In a control group, there is no special treatment. The control group serves as a baseline to compare the other groups to. The diagram below illustrates this:
Notice that all other factors (bacterial strain, concentration of nutrients, concentration of antibiotic added, etc.) are kept the same.
Another term often used in experimentation is null hypothesis. This is different from the scientific hypothesis! Many students get confused by that. The null hypothesis is more of a statistics term and it states that there will be no significant difference observed among the different experimental groups. Scientists usually hope to reject the null hypothesis, which means they do observe a real difference, supporting their scientific hypothesis. This will all become more clear when we walk through some examples.
2017 FRQ - #2 Bees and Caffeine Experiment
This question involves an experiment about bees and the nectar they encounter while pollinating flowers. The scientists want to understand the role of caffeine on the bees’ memory.
The question gives a table showing the results of the experiment, shown below. It includes a control group and test group (caffeine). It also shows the probability of the bees returning to a recently visited nectar source. This probability is used to represent the bees’ short-term and long-term memory.
As you read through the question and think about the experiment, you should consider the set of questions below. Just consider them, no need to write them down. They will help you plan out your responses to the actual problem:
What are these scientists testing in this experiment? In other words, what is their scientific hypothesis?
What is the independent and dependent variable?
What is the difference between the control and test group? What’s the purpose of the control group? Note that sometimes there is more than one test group. Here, we only have one, which is the caffeine treatment group.
What is the null hypothesis?
How could the experimental data be represented graphically?
What do the +/- values mean in each of the data cells?
If you are able to answer all those questions, you will have no trouble with this problem. So let’s answer them:
The scientific hypothesis is that exposure to caffeine is associated with the bees’ memory.
The independent variable is the treatment, which is exposure to caffeine. The dependent variable is what is impacted. Here, that is the bees’ memory.
The control group is exposed to no caffeine, while the treatment group is exposed to caffeine in the nectar. The control group serves as a baseline to compare the treatment group to. If we hypothesize that caffeine has a negative impact on memory, then the probability of revisiting the nectar source should be higher for the treatment compared to the control.
The null hypothesis states that there is no significant difference in memory between the control and treatment groups. Any difference observed would be due to chance. To support the scientific hypothesis, scientists need the data to reject the null hypothesis.
The data here should be represented by a bar graph. There will be two bars, one for control and one for treatment. There should also be error bars because the standard errors are included in the data. The graph would look something like this:
2019 FRQ - #2 Ecological Relationship Between Two Protists
This question is about an experiment that investigates the ecological relationship between two protists. Are they competing for the same food? Does one predate on the other? Or do they live together in harmony and use different resources? That is what the scientists want to know.
The data collected in the experiment is given in the question, shown below.
Let’s answer the same list of questions again to really understand the experiment.
The scientific question being tested is: what kind of ecological relationship do protist species A and B have?
The independent variable is the treatment, which is the two species living together. The dependent variable is the population size of each species over time.
The control group is the species grown separately. The test group is the species grown together.
The null hypothesis states that there is no significant difference in population size between the control and treatment groups at each time point. Any difference observed would be due to chance.
The data here should be represented by a line graph, since we have time as a factor. Time should be on the x-axis -- this is almost always the case. There will be two lines, one for control and one for treatment. The graph would look something like this:
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