Uses of Interface org.apache.commons.statistics.inference.SignificanceResult (Apache Commons Statistics 1.1)

Packages that use SignificanceResult
Package Description

org.apache.commons.statistics.inference
Classes providing hypothesis testing.

Uses of SignificanceResult in org.apache.commons.statistics.inference

Classes in org.apache.commons.statistics.inference that implement SignificanceResult
Modifier and Type	Class	Description
`static class`	`KolmogorovSmirnovTest.OneResult`	Result for the one-sample Kolmogorov-Smirnov test.
`static class`	`KolmogorovSmirnovTest.TwoResult`	Result for the two-sample Kolmogorov-Smirnov test.
`static class`	`MannWhitneyUTest.Result`	Result for the Mann-Whitney U test.
`static class`	`OneWayAnova.Result`	Result for the one-way ANOVA.
`static class`	`TTest.Result`	Result for the t-test.
`static class`	`UnconditionedExactTest.Result`	Result for the unconditioned exact test.
`static class`	`WilcoxonSignedRankTest.Result`	Result for the Wilcoxon signed-rank test.

Methods in org.apache.commons.statistics.inference that return SignificanceResult
Modifier and Type	Method	Description
`SignificanceResult`	BinomialTest.`test(int numberOfTrials, int numberOfSuccesses, double probability)`	Performs a binomial test about the probability of success \( \pi \).
`SignificanceResult`	ChiSquareTest.`test(double[] expected, long[] observed)`	Perform a chi-square goodness-of-fit test evaluating the null hypothesis that the `observed` counts conform to the `expected` counts.
`SignificanceResult`	ChiSquareTest.`test(long[] observed)`	Perform a chi-square goodness-of-fit test evaluating the null hypothesis that the `observed` counts conform to a uniform distribution (each category is equally likely).
`SignificanceResult`	ChiSquareTest.`test(long[][] counts)`	Perform a chi-square test of independence based on the input `counts` array, viewed as a two-way table.
`SignificanceResult`	ChiSquareTest.`test(long[] observed1, long[] observed2)`	Perform a chi-square test of independence of frequency counts in `observed1` and `observed2`.
`SignificanceResult`	FisherExactTest.`test(int[][] table)`	Performs Fisher's exact test on the 2-by-2 contingency table.
`SignificanceResult`	GTest.`test(double[] expected, long[] observed)`	Perform a G-test for goodness-of-fit evaluating the null hypothesis that the `observed` counts conform to the `expected` counts.
`SignificanceResult`	GTest.`test(long[] observed)`	Perform a G-test for goodness-of-fit evaluating the null hypothesis that the `observed` counts conform to a uniform distribution (each category is equally likely).
`SignificanceResult`	GTest.`test(long[][] counts)`	Perform a G-test of independence based on the input `counts` array, viewed as a two-way table.