Testing a hypothesis
A valid hypothesis must be testable. It should also be falsifiable , meaning that it can be disproven by experimental results. Importantly, science does not claim to “prove” anything because scientific understandings are always subject to modification with further information. This step—openness to disproving ideas—is what distinguishes sciences from non-sciences. The presence of the supernatural, for instance, is neither testable nor falsifiable.
To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment.
To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. Experiments typically have a dependent variable , independent variable , and several controlled variables . The dependent variable is some changing aspect of the experiment that you want to find out. For example, if you're testing how a particular drug dosage fights cancer, your dependent variable could be how many cancer cells died. Your independent variable is what you changed to get that result. So in this example, your independent variable would be the different dosages of the drug. A controlled variable is any part of the experimental setup that you kept the same. The more controlled variables you have, the more accurate your data is likely to be. For example, in the drug dosage experiment, maybe you only tested pancreatic cancer patients, who were 60-70 years old, and had an early-stage diagnosis. If you had NO controlled variables, you couldn't be sure whether your results are due to the different drug dosage or something else.
The control group contains every feature of the experimental group except it is not given the manipulation that is hypothesized about. Therefore, if the results of the experimental group differ from the control group, the difference must be due to the hypothesized manipulation, rather than some outside factor. To go back to our previous example, if you had a collection of patients who all received a certain dosage of the drug, and another group who did not receive the drug at all, the latter group is the control group. It's important to use control groups to know whether your experiment results are real. For example, if you saw no difference in cancer cell death between the group who received the drug versus those who did not, that is a clear result indicating the drug at that particular dosage is ineffective. The group who received no treatment is called a negative control , and in drug studies, these groups often receive a placebo , which is a pill/liquid that looks like the drug, but does not actually contain the drug. It contains nothing, or maybe a sugar solution that has no effect on the body. A positive control is a sample/individual or group of samples/individuals who you know will actually demonstrate a difference from the negative control. For example, if you were comparing a new cancer drug to a known drug that works, the individuals receiving the known drug would be the positive control group.