ChiSquareGoodnessOfFitTest Class

public sealed class ChiSquareGoodnessOfFitTest : OneSampleTest<double>

Public NotInheritable Class ChiSquareGoodnessOfFitTest
	Inherits OneSampleTest(Of Double)

public ref class ChiSquareGoodnessOfFitTest sealed : public OneSampleTest<double>

[<SealedAttribute>]
type ChiSquareGoodnessOfFitTest = 
    class
        inherit OneSampleTest<float>
    end

Inheritance

Object  →  HypothesisTest  →  OneSampleTest<Double>  →  ChiSquareGoodnessOfFitTest

Use the ChiSquareGoodnessOfFitTest class to test whether a sample comes from a specific distribution.

The sample can be supplied as either a vector or a vector.

The distribution can be specified as a probability distribution (a class that inherits from Distribution) or a vector. The distribution can be discrete or continuous.

If the sample and distribution are both supplied as a vector, the boundaries must be identical.

A chi-square test uses binned data. A drawback is that the results of the test depend on how the data is binned. The test also needs a suffient sample size for the chi-square approximation to be valid.

The AndersonDarlingTest and OneSampleKolmogorovSmirnovTest tests are alternatives to the chi-square goodness-of-fit test, but they are valid only for continuous distributions.

Reference