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mstats_tests

Module Information

Module Group

Statistical Tests

Purpose

The purpose of this module is to provide the user with the ability to do data sample comparison tests that are available in statsmodels and scipy.stats libraries. The module requires list or parameter value for sample comparison to test statistical hypotheses and is not aimed at dataframe based data

A list of all available activation functions in the module mstats_tests

  •   its_ttest

    data: [list,list] targ:None

    Independent two sample Student's t-test: This test is used to compare the means of two independent samples. It assumes that the data is (normally distributed) and that the (variances of the two groups are equal)

  •   paired_ttest

    data: [list,list] targ:None

    A paired Student's t-test is a statistical test used to determine if there is a significant difference between the means of two related samples. It is used when the data sets are paired or matched in some way, such as when the same group of subjects is measured before and after a treatment or intervention

  •   os_ttest

    data: list targ:popmean

    A one sample Student's t-test is a statistical test used to determine if there is a significant difference between the mean of a sample and a known or hypothesized population mean. It is used when you have one sample of data and want to compare its mean to a specific value.

  •   utest

    data: [list,list] targ:None

    The Mann-Whitney test, also known as the Wilcoxon rank-sum test, is a nonparametric statistical test used to determine whether there is a significant difference between the distributions of two independent samples. It is often used when the data does not meet the assumptions of parametric tests like the t-test

  •   kstest_twosample

    data: [list,list] targ:None

    The Kolmogorov-Smirnov test is a nonparametric statistical test that determines whether a sample comes from a specific distribution. It compares the empirical cumulative distribution function (ECDF) of the sample to the cumulative distribution function (CDF) of the specified distribution

  •   kstest_onesample_normal

    data: list targ:None

    The Kolmogorov-Smirnov test for a normal distribution is a statistical test that determines whether a sample of data comes from a normal distribution

  •   kstest_onesample_uniform

    data: list targ:None

    The Kolmogorov-Smirnov test for a uniform distribution is a statistical test that determines whether a sample of data comes from a uniform distribution

  •   kstest_onesample_exponential

    data: list targ:None

    The Kolmogorov-Smirnov test for a exponential distribution is a statistical test that determines whether a sample of data comes from a exponential distribution

  •   lilliefors_normal

    data: list targ:None

    The Lilliefors test, also known as the Kolmogorov-Smirnov test for normality, is a statistical test used to determine whether a sample of data comes from a normal distribution. It is similar to the Kolmogorov-Smirnov test, but it is specifically designed for testing against a normal distribution.

  •   shapirowilk_normal

    data: list targ:None

    The Shapiro-Wilk test is another statistical test used to determine whether a sample of data comes from a normal distribution

  •   chi2_test

    data: [list,list] targ:None

    The chi-square test is a statistical test used to determine if there is a significant association between two categorical variables

  •   jarquebera_normal

    data: list targ:None

    The Jarque-Bera test is a statistical test used to determine whether a given dataset follows a normal distribution. It is based on the skewness and kurtosis of the data

  •   two_sample_anova¶

    data: [list,list] targ:None

    The ANOVA (Analysis of Variance) test is used to determine if there are any statistically significant differences between the means of two or more groups