- Chi-square test
: "Chi-square test" is often shorthand for
Pearson's chi-square test ."A chi-square test (also chi-squared or chi^2 test) is any
statistical hypothesis test in which the test statistic has achi-square distribution when thenull hypothesis is true, or any in which theprobability distribution of the test statistic (assuming the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough.Some examples of chi-squared tests where the
chi-square distribution is only approximately valid::*Pearson's chi-square test , also known as the chi-square goodness-of-fit test or chi-square test for independence. When mentioned without any modifiers or without other precluding context, this test is usually understood.:*Yates' chi-square test , also known as Yates' correction for continuity.:* Mantel-Haenszel chi-square test.:* Linear-by-linear association chi-square test.:* Theportmanteau test intime-series analysis , testing for the presence ofautocorrelation :*Likelihood-ratio test s in general statistical modelling, for testing whether there is evidence of the need to move from a simple model to a more complicated one (where the simple model is nested within the complicated one).One case where the distribution of the test
statistic is an exactchi-square distribution is the test that the variance of a normally-distributed population has a given value based on asample variance . Such a test is uncommon in practice because values of variances to test against are seldom known exactly.Chi-square test for variance in a normal population
If a sample of size "n" is taken from a population having a
normal distribution , then there is a well-known result (see distribution of the sample variance) which allows a test to be made of whether the variance of the population has a pre-determined value. For example, a manufacturing process might have been in stable condition for a long period, allowing a value for the variance to be determined essentially without error. Suppose that a variant of the process is being tested, giving rise to a small sample of product items whose variation is to be tested. The test statistic "T" in this instance could be set to be the sum of squares about the sample mean, divided by the nominal value for the variance (ie. the value to be tested as holding). Then "T" has a chi-square distribution with "n"–1 degrees of freedom. For example if the sample size is 21, the acceptance region for "T" for a significance level of 5% is the interval 9.59 to 34.17.ee also
*
Pearson's chi-square test for a more detailed explanation
* Generallikelihood-ratio test s, which are approximately chi-square tests
*McNemar's test , related to a chi-square test
* TheWald test , which can be evaluated against a chi-square distributionExternal links
* [http://graphpad.com/quickcalcs/chisquared2.cfm Chi-Square Calculator from GraphPad]
* [http://faculty.vassar.edu/lowry/odds2x2.html Vassar College's 2×2 Chi-Square with Expected Values]References
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*Greenwood, P.E., Nikulin, M.S. (1996) "A guide to chi-squared testing". Wiley, New York. ISBN 047155779X
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