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:
Level of significance
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.