When researchers are trying to determine if changes in one variable lead to changes in another variable, they must perform experiments in order to establish a causal relationship. Other research methods (such as correlational studies) can be used to establish that a relationship between to variables exists, but an actual experiment is necessary to establish that it is a *cause-and-effect *type of relationship.

Experiments can be extremely complex and included a multitude of variables. However, one of the most basic methods is to use what is known as a simple experiment.

### What is a Simple Experiment?

A simple experiment can establish cause-and-effect, so this type of study is often used to determine the effect of a treatment. For examples, researchers might want to determine if administering a certain type of medicine leads to an improvement of symptoms. In a simple experiment, study participants are randomly assigned to one of two groups. Generally, one group is the control group and receives no treatment, while the other group is the experimental group and receives the treatment.

### Parts of a Simple Experiment

The simple experiment is composed of a few key elements:

**The experimental hypothesis:** A statement that predicts that the treatment will cause an effect. The experimental hypothesis will always be phrased as a cause-and-effect statement. For example, researchers might propose a hypothesis that: "Administration of Medicine A will result in a reduction of symptoms of Disease B."

**The null hypothesis:** A hypothesis that the experimental treatment will have no effect on the participants or dependent variables. It is 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 variable:** The treatment variable that is manipulated by the experimenter.

**The dependent variable:** The response that the experimenter is measuring.

**The control group:** This group is made up of individuals who are randomly assigned to a group but do not receive the treatment. The measures takes from the control group are then compared to those in the experimental group to determine if the treatment had an effect.

**The experimental group:** This group is made up of individuals who are randomly assigned to the group and then receive the treatment. The scores of these participants are compared to those in the control group to determine if the treatment had an effect.

### 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. How do researchers determine this effect? Due to the always present possibility of errors, we can never be 100% sure of the relationship between two variables. After all, there might always exist some unknown variables that we are unaware of or unable to measure that might nevertheless have an influence over the outcomes. Despite this ever-present problem, there are ways to determine if there *most likely* is a meaningful relationship.

Experimenters use inferential statistics to determine if the results of an experiment are meaningful. Inferential statistics is a branch of science that deals with drawing inferences about a population based upon 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 if the particular results are due merely to chance, the probability of obtaining these results would be less than 5%. Occasionally, smaller p-values are seen such as p < 0.01. There are a number of different means of measuring statistical significance. The type of statistical test used depends largely upon the type of research design that was used.