A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. These two statements are called the null hypothesis and the alternative hypothesis.

Hypothesis tests are not 100% accurate because they use a random sample to draw conclusions about entire populations. When you perform a hypothesis test, there are two types of errors related to drawing an incorrect conclusion.

- Type I error: The rejects a null hypothesis that is true. You can think of this as a false positive.
- Type II error: The test fails to reject a null hypothesis that is false. You can think of this as a false negative.

A test result is statistically significant when the sample statistic is unusual enough relative to the null hypothesis that you can reject the null hypothesis for the entire population. “Unusual enough” in a hypothesis test is defined by how unlikely the effect observed in your sample is if the null hypothesis is true.

If your sample data provide sufficient evidence, you can reject the null hypothesis for the entire population. Your data favor the alternative hypothesis.