anova | % Perform a one-way analysis of variance (ANOVA). The goal is to test |

bartlett_test | % Perform a Bartlett test for the homogeneity of variances in the data |

chisquare_test_homogeneity | % Given two samples @var{x} and @var{y}, perform a chisquare test for |

chisquare_test_independence | % Perform a chi-square test for independence based on the contingency |

cor_test | % Test whether two samples @var{x} and @var{y} come from uncorrelated |

f_test_regression | % Perform an F test for the null hypothesis rr * b = r in a classical |

hotelling_test | % For a sample @var{x} from a multivariate normal distribution with unknown |

hotelling_test_2 | % For two samples @var{x} from multivariate normal distributions with |

kolmogorov_smirnov_test | % Perform a Kolmogorov-Smirnov test of the null hypothesis that the |

kolmogorov_smirnov_test_2 | % Perform a 2-sample Kolmogorov-Smirnov test of the null hypothesis |

kruskal_wallis_test | % Perform a Kruskal-Wallis one-factor 'analysis of variance'. |

manova | % Perform a one-way multivariate analysis of variance (MANOVA). The |

mcnemar_test | % For a square contingency table @var{x} of data cross-classified on |

prop_test_2 | % If @var{x1} and @var{n1} are the counts of successes and trials in |

run_test | % Perform a chi-square test with 6 degrees of freedom based on the |

sign_test | % For two matched-pair samples @var{x} and @var{y}, perform a sign test |

t_test | % For a sample @var{x} from a normal distribution with unknown mean and |

t_test_2 | % For two samples x and y from normal distributions with unknown means |

t_test_regression | % Perform an t test for the null hypothesis @code{@var{rr} * @var{b} = |

u_test | % For two samples @var{x} and @var{y}, perform a Mann-Whitney U-test of |

var_test | % For two samples @var{x} and @var{y} from normal distributions with |

welch_test | % For two samples @var{x} and @var{y} from normal distributions with |

wilcoxon_test | % For two matched-pair sample vectors @var{x} and @var{y}, perform a |

z_test | % Perform a Z-test of the null hypothesis @code{mean (@var{x}) == |

z_test_2 | % For two samples @var{x} and @var{y} from normal distributions with |