Hypothesis Tests

t-test, F-test, chi-square goodness-of-fit test, and more

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

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adtestAnderson-Darling test
chi2gofChi-square goodness-of-fit test
crosstabCross-tabulation
dwtestDurbin-Watson test with residual inputs
fishertestFisher’s exact test
jbtestJarque-Bera test
kstestOne-sample Kolmogorov-Smirnov test
kstest2Two-sample Kolmogorov-Smirnov test
lillietestLilliefors test
runstestRun test for randomness
friedmanFriedman’s test
kruskalwallisKruskal-Wallis test
multcompareMultiple comparison test
ranksumWilcoxon rank sum test
sampsizepwrSample size and power of test
signrankWilcoxon signed rank test
signtestSign test
ttestOne-sample and paired-sample t-test
ttest2Two-sample t-test
ztestz-test
ansaribradleyAnsari-Bradley test
barttestBartlett’s test
sampsizepwrSample size and power of test
vartestChi-square variance test
vartest2Two-sample F-test for equal variances
vartestn Multiple-sample tests for equal variances

Topics

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.

Available Hypothesis Tests

Tests of distributions and statistics

Featured Examples

Selecting a Sample Size

Selecting a Sample Size

Determine the number of samples or observations needed to carry out a statistical test. It illustrates sample size calculations for a simple problem, then shows how to use the sampsizepwr function to compute power and sample size for two more realistic problems. Finally, it illustrates the use of Statistics and Machine Learning Toolbox™ functions to compute the required sample size for a test that the sampsizepwr function does not support.

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Statistics and Machine Learning Toolbox Documentation

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