Key Takeaways
- A simple experiment tests cause and effect by changing one thing to see if it affects another.
- The control group does not get the treatment, while the experimental group does.
- Statistical significance shows if the results are not just by chance, with p < 0.05 meaning it is likely not random.
A simple experiment is used by researchers to determine if changes in one variable lead to changes in another variable — in other words, to establish cause-and-effect.
For example, in a simple experiment looking at the effectiveness of a new medication, study participants would be randomly assigned to one of two groups: one of these would be the control group and receive no treatment, while the other group would be the experimental group that receives the treatment being studied.
The Elements of a Simple Experiment
A simple experiment is composed of several key elements:
- The Experimental Hypothesis: This is a statement that predicts the treatment will cause an effect. It will always be phrased as a cause-and-effect statement. For example, researchers might phrase a hypothesis as: “Administration of Medicine A will result in a reduction of symptoms of Disease B.”
- The Null Hypothesis: This is a hypothesis stating the experimental treatment will have no effect on the participants or dependent variables. It’s important to note that failing to find an effect of the treatment does not mean there is no effect. The treatment might impact another variable the researchers are not measuring in the current experiment.
- The Independent Variable: The treatment variable being manipulated by the experimenter.
- The Dependent Variable: The response the researchers are measuring.
- The Control Group: The group of randomly assigned individuals who do not receive the treatment. The measurements taken from the control group will be compared to those in the experimental group to determine if the treatment had an effect.
- The Experimental Group: This group of randomly assigned individuals who will receive the treatment being tested.
Determining the Results of a Simple Experiment
Once the data from the simple experiment is gathered, researchers compare the results of the experimental group to those of the control group to determine if the treatment had an effect. Due to the omnipresent possibility of errors, it’s not possible to be 100 percent sure of the relationship between two variables. There can always be be unknown variables influencing the outcome of the experiment.
Despite this challenge, there are ways to determine if there most likely is a meaningful relationship between the variables. To do this, scientists use inferential statistics—a branch of science that deals with drawing inferences about a population based on measurements taken from a representative sample of that population.
The key to determining if a treatment had an effect is to measure the statistical significance. Statistical significance shows that the relationship between the variables is probably not due to mere chance and that a real relationship most likely exists between the two variables.
Statistical significance is often represented like this:
p < 0.05
A p-value of less than .05 indicates that the results likely are due to chance and that the probability of obtaining these results would be less than 5%.
There are a number of different means of measuring statistical significance. The one used will depend on the type of research design that was used for the experiment.
Verywell Mind uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy.
Skelly AC. Probability, proof, and clinical significance. Evid Based Spine Care J. 2011;2(4):9-11. doi:10.1055/s-0031-1274751
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