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.
|chi2gof||Chi-square goodness-of-fit test|
|fishertest||Fisher's exact test|
|kstest||One-sample Kolmogorov-Smirnov test|
|kstest2||Two-sample Kolmogorov-Smirnov test|
|runstest||Run test for randomness|
|multcompare||Multiple comparison test|
|ranksum||Wilcoxon rank sum test|
|sampsizepwr||Sample size and power of test|
|signrank||Wilcoxon signed rank test|
|ttest||One-sample and paired-sample t-test|