# Hypothesis Tests

Statistics and Machine Learning Toolbox™ provides parametric and nonparametric hypothesis tests to help you determine if your sample data comes from a population with particular characteristics.

Distribution tests, such as Anderson-Darling and one-sample Kolmogorov-Smirnov, test whether sample data comes from a population with a particular distribution. Test whether two sets of sample data have the same distribution using tests such as two-sample Kolmogorov-Smirnov.

Location tests, such as *z*-test and one-sample *t*-test,
test whether sample data comes from a population with a particular
mean or median. Test two or more sets of sample data for the same
location value using a two-sample *t*-test or multiple
comparison test.

Dispersion tests, such as Chi-square variance, test whether sample data comes from a
population with a particular variance. Compare the variances of two or more sample
data sets using a two-sample *F*-test or multiple-sample test.

Determine additional features of sample data by cross-tabulating, conducting a run test for randomness, and determine the sample size and power for a hypothesis test.

## Functions

## Topics

**Available Hypothesis Tests**View hypothesis tests of distributions and statistics.

**Hypothesis Testing**Hypothesis testing is a common method of drawing inferences about a population based on statistical evidence from a sample.

**Hypothesis Test Terminology**All hypothesis tests share the same basic terminology and structure.

**Hypothesis Test Assumptions**Different hypothesis tests make different assumptions about the distribution of the random variable being sampled in the data.