Chisquared test
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A chisquared test, also referred to as chisquare test or test, is any statistical hypothesis test in which the sampling distribution of the test statistic is a chisquared distribution when the null hypothesis is true. Also considered a chisquared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chisquared distribution as closely as desired by making the sample size large enough.
Some examples of chisquared tests where the chisquared distribution is only approximately valid:
 Pearson's chisquared test, also known as the chisquared goodnessoffit test or chisquared test for independence. When the chisquared test is mentioned without any modifiers or without other precluding context, this test is usually meant (for an exact test used in place of , see Fisher's exact test).
 Yates's correction for continuity, also known as Yates' chisquared test.
 Cochran–Mantel–Haenszel chisquared test.
 McNemar's test, used in certain 2 × 2 tables with pairing
 Tukey's test of additivity
 The portmanteau test in timeseries analysis, testing for the presence of autocorrelation
 Likelihoodratio tests 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 exact chisquared distribution is the test that the variance of a normally distributed population has a given value based on a sample variance. Such a test is uncommon in practice because values of variances to test against are seldom known exactly.
Chisquared 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 result (see distribution of the sample variance) which allows a test to be made of whether the variance of the population has a predetermined 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 n 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 (i.e. the value to be tested as holding). Then T has a chisquared 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.
See also
 Chisquared test nomogram
 Gtest
 Minimum chisquare estimation
 The Wald test can be evaluated against a chisquared distribution.
References
 Weisstein, Eric W., "ChiSquared Test", MathWorld.
 Corder, G.W., Foreman, D.I. (2009). Nonparametric Statistics for NonStatisticians: A StepbyStep Approach Wiley, ISBN 9780470454619
 Greenwood, P.E., Nikulin, M.S. (1996) A guide to chisquared testing. Wiley, New York. ISBN 047155779X
 Nikulin, M.S. (1973). "Chisquared test for normality". In: Proceedings of the International Vilnius Conference on Probability Theory and Mathematical Statistics, v.2, pp. 119–122.
 Bagdonavicius, V., Nikulin, M.S. (2011) "Chisquare goodnessoffit test for right censored data". The International Journal of Applied Mathematics and Statistics, p. 3050.^{full citation needed}

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