# Hypothesis Testing

Goodness-of-fit tests such as Chi-square, Kolmogorov-Smirnov, Shapiro-Wilk, and t-test

### Note

MuPAD® notebooks will be removed in a future release. Use MATLAB® live scripts instead.

To convert a MuPAD notebook file to a MATLAB live script file, see `convertMuPADNotebook`. MATLAB live scripts support most MuPAD functionality, although there are some differences. For more information, see Convert MuPAD Notebooks to MATLAB Live Scripts.

 `stats::csGOFT` Classical chi-square goodness-of-fit test `stats::equiprobableCells` Divide the real line into equiprobable intervals `stats::ksGOFT` The Kolmogorov-Smirnov goodness-of-fit test `stats::swGOFT` The Shapiro-Wilk goodness-of-fit test for normality `stats::tTest` T-test for a mean

## Examples and How To

Perform chi-square Test

For the classical chi-square goodness-of-fit test, MuPAD provides the `stats::csGOFT` function.

Perform Kolmogorov-Smirnov Test

For the Kolmogorov-Smirnov goodness-of-fit test, MuPAD provides the `stats::ksGOFT` function.

Perform Shapiro-Wilk Test

The Shapiro-Wilk goodness-of-fit test asserts the hypothesis that the data has a normal distribution.

Perform t-Test

The t-Test compares the actual mean value of a data sample with the specified value `m`.

## Concepts

Principles of Hypothesis Testing

Hypothesis (goodness-of-fit) testing is a common method that uses statistical evidence from a sample to draw a conclusion about a population.

#### Mathematical Modeling with Symbolic Math Toolbox

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