## Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) (Figure 1.2), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. Imagine that one morning when you wake up and flip a the switch to turn on your bedside lamp, the light won’t turn on. That is an observation that also describes a problem: the lights won’t turn on. Of course, you would next ask the question: “Why won’t the light turn on?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The light won’t turn on because the bulb is burned out.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The light won’t turn on because the lamp is unplugged” or “The light won’t turn on because the power is out.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable, meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Red is a better color than blue.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Once a hypothesis has been selected, a prediction can be made that predicts what you would observe if you tested this hypothesis. A prediction is different from a hypothesis because a prediction describes what you will actually observe in your experiment. The hypothesis is the reason why you will observe your prediction. Your prediction helps you to begin designing your experiment by determining specifically what you will be testing.

A variable is any part of the experiment that can vary or change during the experiment. Typically, an experiment only tests one variable and all the other conditions in the experiment are held constant. The variable that is tested is known as the independent variable. A constant is a condition that is the same between all of the tested groups. The dependent variable is the thing (or things) that you are measuring as the outcome of your experiment. A prediction often has the format “If [I change the independent variable in this way] then [I will observe that the dependent variable does this]” For example, the prediction for the first hypothesis might be, “If you change the light bulb, then the light will turn on.” In this experiment, the independent variable (the thing that you are testing) would be changing the light bulb and the dependent variable is whether or not the light turns on. It would be important to hold all the other aspects of the environment constant, for example not messing with the lamp cord or trying to turn the lamp on using a different light switch.

We can put the experiment with the light that won’t go in into the figure above:

1. Observation: the light won’t turn on.
2. Question: why won’t the light turn on?
3. Hypothesis: the lightbulb is burned out.
4. Prediction: if I change the lightbulb (independent variable), then the light will turn on (dependent variable).
5. Experiment: change the lightbulb while leaving all other variables the same.
6. Analyze the results: the light didn’t turn on.
7. Results do not support the hypothesis, time to develop a new one!
8. Hypothesis 2: the lamp is unplugged.
9. Prediction 2: if I plug in the lamp, then the light will turn on.
10. Experiment: plug in the lamp
11. Analyze the results: the light turned on!
12. Results support the hypothesis, it’s time to move on to the next experiment!

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Another important aspect of designing an experiment is the presence of one or more control groups. A control group is a sample that is not treated with the independent variable, but is otherwise treated the same way as your experimental sample.

### Example 1

Tomatoes fertilized with Brand A produced an average of 20 tomatoes per plant, while tomatoes fertilized with Brand B produced an average of 10 tomatoes per plant. You’d want to use Brand A next time you grow tomatoes, right? But what if I told you that plants grown without fertilizer produced an average of 30 tomatoes per plant! Now what will you use on your tomatoes?

### Example 2

You are interested in testing a new brand of natural cleaning product. You spray it around your kitchen sink and then take a sample of the bacteria remaining near the drain. You find, to your horror, that there are still 100 bacteria per square inch after cleaning! That seems awful, unless you have the proper control to compare it to: the number of bacteria present on the surface before it was cleaned. According to WebMD, there are more than 500,000 bacteria per square inch around kitchen drains. That means the cleaner actually killed well over 99.9% of the bacteria around the drain.

# References

OpenStax, Biology. OpenStax CNX. May 27, 2016 http://cnx.org/contents/s8Hh0oOc@9.10:RD6ERYiU@5/The-Process-of-Science.