One important way to draw conclusions about the properties of a population is with hypothesis testing. You can use *hypothesis tests* to compare a population measure to a specified value, compare measures for two populations, determine whether a population follows a specified probability distribution, and so forth.

Hypothesis testing is conducted as a six-step procedure:

Null hypothesis

Alternative hypothesis

Level of significance

Test statistic

Critical value

Decision

The *null hypothesis* is a statement that’s assumed to be true unless there’s *strong* evidence against it. The *alternative hypothesis* is a statement that is accepted if the null hypothesis is rejected. The *level of significance* specifies the likelihood of rejecting the null hypothesis when it’s true; this is known as a *Type I Error.*

The *test statistic* is a numerical measure you compute from sample data to determine whether or not the null hypothesis should be rejected. The *critical value* is used as a benchmark to determine whether the test statistic is too extreme to be consistent with the null hypothesis.

The *decision* as to whether or not the null hypothesis should be rejected is determined as follows:

If the absolute value of the test statistic exceeds the absolute value of the critical value, the null hypothesis is rejected.

Otherwise, the null hypothesis fails to be rejected.