AndersonDarlingTest Class

[SerializableAttribute]
public class AndersonDarlingTest : HypothesisTest<AndersonDarlingDistribution>, 
	IHypothesisTest<AndersonDarlingDistribution>, IHypothesisTest

<SerializableAttribute>
Public Class AndersonDarlingTest
	Inherits HypothesisTest(Of AndersonDarlingDistribution)
	Implements IHypothesisTest(Of AndersonDarlingDistribution), IHypothesisTest

// Test against a standard Uniform distribution
// References: http://www.math.nsysu.edu.tw/~lomn/homepage/class/92/kstest/kolmogorov.pdf

// Suppose we got a new sample, and we would like to test whether this
// sample seems to have originated from a uniform continuous distribution.
// 
double[] sample = 
{ 
    0.621, 0.503, 0.203, 0.477, 0.710, 0.581, 0.329, 0.480, 0.554, 0.382
};

// First, we create the distribution we would like to test against:
// 
var distribution = UniformContinuousDistribution.Standard;

// Now we can define our hypothesis. The null hypothesis is that the sample
// comes from a standard uniform distribution, while the alternate is that
// the sample is not from a standard uniform distribution.
// 
var adtest = new AndersonDarlingTest(sample, distribution);

double statistic = adtest.Statistic; //  1.3891622091168489561
double pvalue = adtest.PValue; // 0.2052

bool significant = adtest.Significant; // false

// Since the null hypothesis could not be rejected, then the sample
// can perhaps be from a uniform distribution. However, please note
// that this doesn't means that the sample *is* from the uniform, it
// only means that we could not rule out the possibility.

// Test against a Normal distribution

// This time, let's see if the same sample from the previous example
// could have originated from a standard Normal (Gaussian) distribution.
// 
double[] sample =
{ 
    0.621, 0.503, 0.203, 0.477, 0.710, 0.581, 0.329, 0.480, 0.554, 0.382
};

// Let's estimate a new Normal distribution using the sample
NormalDistribution distribution = NormalDistribution.Estimate(sample);

// Now, we can create a new Anderson-Darling's test:
var ad = new AndersonDarlingTest(sample, distribution);

// We can then compute the test statistic, 
// the test p-value and its significance:
double statistic = ad.Statistic; // 0.1796
double pvalue = ad.PValue; // 0.8884
bool significant = ad.Significant; // false