Common interface for analyses of variance.
IHypothesisTestCommon interface for Hypothesis tests depending on a statistical distribution.
IHypothesisTestTDistributionCommon interface for Hypothesis tests depending on a statistical distribution.
Enumerations RemarksThis namespace contains a suite of parametric and non-parametric hypothesis tests. Every test in this library implements the IHypothesisTest interface, which defines a few key methods and properties to assert whether an statistical hypothesis can be supported or not. Every hypothesis test is associated with an statistic distribution which can in turn be queried, inspected and computed as any other distribution in the Accord.Statistics.Distributionsnamespace.
By default, tests are created using a 0.05 significance level , which in the framework is referred as the test's size. P-Values are also ready to be inspected by checking a test's P-Value property.
Furthermore, several tests in this namespace also support power analysis. The power analysis of a test can be used to suggest an optimal number of samples which have to be obtained in order to achieve a more interpretable or useful result while doing hypothesis testing. Power analyses implement the IPowerAnalysis interface, and analyses are available for the one sample Z, and T tests, as well as their two sample versions.
Some useful parametric tests are the BinomialTest, ChiSquareTest, FTest, MultinomialTest, TTest, WaldTest and ZTest. Useful non-parametric tests include the KolmogorovSmirnovTest, SignTest, WilcoxonSignedRankTest and the WilcoxonTest.
Tests are also available for two or more samples. In this case, we can find two sample variants for the PairedTTest, TwoProportionZTest, TwoSampleKolmogorovSmirnovTest, TwoSampleSignTest, TwoSampleTTest, TwoSampleWilcoxonSignedRankTest, TwoSampleZTest, as well as the MannWhitneyWilcoxonTest for unpaired samples. For multiple samples we can find the OneWayAnova and TwoWayAnova, as well as the LeveneTest and BartlettTest.
Finally, the namespace also includes several tests for contingency tables. Those tests include Kappa test for inter-rater agreement and its variants, such as the AverageKappaTest, TwoAverageKappaTest and TwoMatrixKappaTest. Other tests include BhapkarTest, McNemarTest, ReceiverOperatingCurveTest, StuartMaxwellTest, and the TwoReceiverOperatingCurveTest.
The namespace class diagram is shown below.
Please note that class diagrams for each of the inner namespaces are also available within their own documentation pages.
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