**Two-tailed Test**

When testing a hypothesis, you must determine if it is a one-tailed or a two-tailed test. The most common format is a two-tailed test, meaning the critical region is located in both tails of the distribution. This is also referred to as a *non-directional* hypothesis.

This type of test is associated with a "neutral" alternative hypothesis. Here are some examples:

- There
*is a difference* between the scores.
- The groups are
*not equal*.
- There
*is a relationship* between the variables.

**One-tailed Test**

The alternative option is a one-tailed test. As the name implies, the critical region lies in only one tail of the distribution. This is also called a *directional* hypothesis. The image below shows a right-tailed test. A left-tailed test would be another type of one-tailed test.

This type of test is associated with a more specific alternative claim. Here are some examples:

- One group is
*higher *than the other.
- There is a
*decrease *in performance.
- Group A performs
*worse than *Group B.