Hippocampus Biology: How Do Biologists Study?

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Now that we know something about biology as a field of study, let's go back to the problems caused by large numbers of deer and see how biologists actually do their work.

Biology is a problem-solving process, not a collection of facts. This is another of the important themes we'll use throughout the course. Biologists use a problem-solving process called the scientific method.

The first step in investigating any scientific issue is to develop a clear statement of the problem. Sometimes it helps to divide the big problem into smaller, more manageable chunks. Suppose we just concentrate on the problem of Lyme disease. The underlying problem is that the incidence of Lyme disease is at an all-time high.

Once the problem is clearly stated, a scientist proposes a hypothesis. A hypothesis is a tentative, untested explanation of a scientific issue. Scientists proposed that the deer in the area were causing the increase in Lyme disease. Causation is the term scientists use to claim that one thing, like deer, causes something else to happen, like Lyme disease. It's the act or process of causing something to happen.

Now that we've learned about causation, let's return to the lab. The scientists' hypothesis didn't just come out of thin air. Like all hypotheses, it was based on verifiable observations. First, Lyme disease is more common among people who spend a lot of time in the woods, where there are large deer populations. Second, Lyme disease cases increase when the deer population increases. Let's look more closely at this second observation or fact.

Scientists made this observation by looking at a graph of Lyme disease cases versus deer population. When the deer population increases, the number of Lyme disease cases also increases. This graph shows that there's a correlation between the deer population and the number of Lyme disease cases. A correlation is a statistical comparison of two variables, such as Lyme disease cases and the deer population. A high correlation, like the one we see here, means there is a strong relationship between the two variables. A high correlation produces a graph where all the points lie very close to the line. Does a high correlation prove that deer cause Lyme disease? Not quite!

Just because two variables are highly correlated, it doesn't mean that one causes the other. Another way of saying this is that correlation DOES NOT equal causation!

See if you understand the difference between causation and correlation. Read the research results, then answer yes or no to the questions that follow. Click Submit to see if you're correct. Click "Jump Ahead" to skip this step.

All we have at this point is a theory. A theory is a tested explanation of facts, observations, and natural phenomena. A theory can't be proven absolutely, because it's always possible that further observations or experiments will prove the theory to be limited, or that it will be replaced by a better theory.

Good scientific practice depends on establishing a cause-and-effect relationship between two variables. To provide more evidence for the theory that deer cause Lyme disease, biologists have to conduct controlled experiments. Controlled scientific experiments are ones where all factors are the same between two test situations, except for the single experimental variable. Controlled experiments can be hard to carry out in the real world. Think about all the variables that might change when the deer population changes. Deer might be plentiful because food is plentiful. But this can increase the population of organisms other than just deer. Could one of these organisms be the cause of Lyme disease?

Controlled experiments are a critical part of the scientific method. If the experimental results are consistent with the hypothesis, scientists repeat the experiment to make sure their results are reproducible. Once the results have been reproduced, the hypothesis is now considered a fact. A fact is a piece of information that has been verified as true or correct. If the results aren't consistent with the hypothesis, scientists revise the hypothesis and then design, conduct, and evaluate new experiments.

The scientists working to find the cause of Lyme disease had to revise and test their hypothesis many times before they solved the problem. Even though they observed a high correlation between the deer population and the number of Lyme disease cases, it turned out that their original hypothesis was incorrect. Correlation doesn't equal causation!

Scientists sometimes see a high correlation between two variables only because both variables are highly correlated with some other unknown variable. That's exactly what happened while scientists worked to solve the mystery of Lyme disease.

We now know that the unknown variable in Lyme disease is a bacterium called Borrelia burgdorferi. The bacteria are carried by ticks. The infected ticks feed on small mammals like mice and large mammals such as deer and humans. Now we can see how the correlation between the deer population and the number of Lyme diseases cases makes sense, even though deer don't cause the disease! The more deer there are, the more plentiful the food supply for the disease-carrying ticks. As the number of infected ticks increases, so does the incidence of the disease.