public void ConstructorTest3()
{
// Example based on Wikipedia's article for χ²-Squared Test
// http://en.wikipedia.org/wiki/Pearson's_chi-squared_test
// Suppose we would like to test the hypothesis that a random sample of
// 100 people has been drawn from a population in which men and women are
// equal in frequency.
// Under this hypothesis, the observed number of men and women would be
// compared to the theoretical frequencies of 50 men and 50 women. So,
// after drawing our sample, we found out that there were 44 men and 56
// women in the sample:
// man woman
double[] observed = { 44, 56 };
double[] expected = { 50, 50 };
// If the null hypothesis is true (i.e., men and women are chosen with
// equal probability), the test statistic will be drawn from a chi-squared
// distribution with one degree of freedom. If the male frequency is known,
// then the female frequency is determined.
//
int degreesOfFreedom = 1;
// So now we have:
//
var chi = new ChiSquareTest(expected, observed, degreesOfFreedom);
// The chi-squared distribution for 1 degree of freedom shows that the
// probability of observing this difference (or a more extreme difference
// than this) if men and women are equally numerous in the population is
// approximately 0.23.
double pvalue = chi.PValue; // 0.23
// This probability is higher than conventional criteria for statistical
// significance (0.001 or 0.05), so normally we would not reject the null
// hypothesis that the number of men in the population is the same as the
// number of women.
bool significant = chi.Significant; // false
Assert.AreEqual(0.23013934044341644, pvalue);
Assert.IsFalse(significant);
}
