fairlens.metrics.significance#
Collection of methods which can be used to numerically or analytically compute p-values and confidence intervals.
- This module provides three functions to sample and generate distributions required for estimating p_values:
permutation_statistic
bootstrap_statistic
bootstrap_binned_statistic
The functions, resampling_p_value, resampling_interval can be use these distributions to carry out p-value tests or obtain a confidence interval.
Functions
Calculate an approximate confidence interval for a binomial proportion of a sample. |
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Calculate an exact p-value for an observed binomial proportion of a sample. |
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Compute the samples of a binned statistic estimate using the bootstrap method. |
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Compute the samples of a statistic estimate using the bootstrap method. |
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Compute the non-parametric Brunner-Munzel test of the hypothesis that the probability of getting large values in the target attribute distributions (determined by the input groups of interest) is equal, without requiring equivariance. |
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Performs the sampling for a two sample permutation test. |
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Compute a confidence interval using a distribution of the test statistic on resampled data. |
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Compute a p-value using a resampled test statistic distribution. |