The Simple Experiment

How Researchers Discover Cause-and-Effect Relationships

Student conducting a simple experiment
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A simple experiment is one researchers often use to determine if changes in one variable might lead to changes in another variable—in other words, to establish cause-and-effect.  In a simple experiment looking at the effectiveness of a new medication, for instance, study participants may 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 severeal key elements:

  • The experimental hypothesis. This is a statement that predicts that the treatment will cause an effect and so will always be phrased as a cause-and-effect statement. For example, researchers might phrase a hypothesis in this way: "Administration of Medicine A will result in a reduction of symptoms of Disease B."
  • The null hypothesis. This is a hypothesis that 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 that there is no effect. The treatment might impact another variable that the researchers are not measuring in the current experiment.
  • The independent variableThe treatment variable that is manipulated by the experimenter.
  • The dependent variable. This refers to the response the researchers are measuring.
  • The control group. These are the individuals who are randomly assigned to a group but 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 study participants is made up of the randomly-selected subjects who will receive the treatment being tested. 

    Determining the Results of a Simple Experiment

    Once the data from the simple experiment has been gathered, researchers then compare the results of the experimental group to those of the control group to determine if the treatment had an effect. Due to the always present possibility of errors, it's not possible to be 100 percent sure of the relationship between two variables. There might be unknown variables at play that influence the outcome of the experiment, for example.

    Despite this challenge, there are ways to determine if there most likely is a meaningful relationship. To do this, scientists use inferential statistics—a branch of science that deals with drawing inferences about a population based on measures 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 five percent.

    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.

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