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

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
(package private) class	BaseSignificanceResult	Base implementation for the result of a test for significance.
static class	KolmogorovSmirnovTest.OneResult	Result for the one-sample Kolmogorov-Smirnov test.
static final class	KolmogorovSmirnovTest.TwoResult	Result for the two-sample Kolmogorov-Smirnov test.
static final class	MannWhitneyUTest.Result	Result for the Mann-Whitney U test.
static final class	OneWayAnova.Result	Result for the one-way ANOVA.
static final class	TTest.Result	Result for the t-test.
static final class	UnconditionedExactTest.Result	Result for the unconditioned exact test.
static final 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.