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Pearson's chisquared test (χ^{2}) is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. It is suitable for unpaired data from large samples.^{[1]} It is the most widely used of many chisquared tests (Yates, likelihood ratio, portmanteau test in time series, etc.) – statistical procedures whose results are evaluated by reference to the chisquared distribution. Its properties were first investigated by Karl Pearson in 1900.^{[2]} In contexts where it is important to improve a distinction between the test statistic and its distribution, names similar to Pearson χsquared test or statistic are used.
It tests a null hypothesis stating that the frequency distribution of certain events observed in a sample is consistent with a particular theoretical distribution. The events considered must be mutually exclusive and have total probability 1. A common case for this is where the events each cover an outcome of a categorical variable. A simple example is the hypothesis that an ordinary sixsided die is "fair", i. e., all six outcomes are equally likely to occur.
Pearson's chisquared test is used to assess two types of comparison: tests of goodness of fit and tests of independence.
The procedure of the test includes the following steps:
In this case observations are divided among cells. A simple application is to test the hypothesis that, in the general population, values would occur in each cell with equal frequency. The "theoretical frequency" for any cell (under the null hypothesis of a discrete uniform distribution) is thus calculated as
and the reduction in the degrees of freedom is , notionally because the observed frequencies are constrained to sum to .
When testing whether observations are random variables whose distribution belongs to a given family of distributions, the "theoretical frequencies" are calculated using a distribution from that family fitted in some standard way. The reduction in the degrees of freedom is calculated as , where is the number of covariates used in fitting the distribution. For instance, when checking a threecovariate Weibull distribution, , and when checking a normal distribution (where the parameters are mean and standard deviation), . In other words, there will be degrees of freedom, where is the number of categories.
It should be noted that the degrees of freedom are not based on the number of observations as with a Student's t or Fdistribution. For example, if testing for a fair, sixsided die, there would be five degrees of freedom because there are six categories/parameters (each number). The number of times the die is rolled will have absolutely no effect on the number of degrees of freedom.
The value of the teststatistic is
where
The chisquared statistic can then be used to calculate a pvalue by comparing the value of the statistic to a chisquared distribution. The number of degrees of freedom is equal to the number of cells , minus the reduction in degrees of freedom, .
The result about the numbers of degrees of freedom is valid when the original data are multinomial and hence the estimated parameters are efficient for minimizing the chisquared statistic. More generally however, when maximum likelihood estimation does not coincide with minimum chisquared estimation, the distribution will lie somewhere between a chisquared distribution with and degrees of freedom (See for instance Chernoff and Lehmann, 1954).
In Bayesian statistics, one would instead use a Dirichlet distribution as conjugate prior. If one took a uniform prior, then the maximum likelihood estimate for the population probability is the observed probability, and one may compute a credible region around this or another estimate.
In this case, an "observation" consists of the values of two outcomes and the null hypothesis is that the occurrence of these outcomes is statistically independent. Each observation is allocated to one cell of a twodimensional array of cells (called a contingency table) according to the values of the two outcomes. If there are r rows and c columns in the table, the "theoretical frequency" for a cell, given the hypothesis of independence, is
where is the total sample size (the sum of all cells in the table). With the term "frequencies" this page does not refer to already normalised values.
The value of the teststatistic is
Fitting the model of "independence" reduces the number of degrees of freedom by p = r + c − 1. The number of degrees of freedom is equal to the number of cells rc, minus the reduction in degrees of freedom, p, which reduces to (r − 1)(c − 1).
For the test of independence, also known as the test of homogeneity, a chisquared probability of less than or equal to 0.05 (or the chisquared statistic being at or larger than the 0.05 critical point) is commonly interpreted by applied workers as justification for rejecting the null hypothesis that the row variable is independent of the column variable.^{[3]} The alternative hypothesis corresponds to the variables having an association or relationship where the structure of this relationship is not specified.
The chisquared test, when used with the standard approximation that a chisquared distribution is applicable, has the following assumptions:^{[citation needed]}
A test that relies on different assumptions is Fisher's exact test; if its assumption of fixed marginal distributions is met it is substantially more accurate in obtaining a significance level, especially with few observations. In the vast majority of applications this assumption will not be met, and Fisher's exact test will be over conservative and not have correct coverage.^{[citation needed]}
In this context, the frequencies of both theoretical and empirical distributions are unnormalised counts, and for a chisquared test the total sample sizes of both these distributions (sums of all cells of the corresponding contingency tables) have to be the same.
For example, 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, the observed number of men and women would be compared to the theoretical frequencies of 50 men and 50 women. If there were 44 men in the sample and 56 women, then
If the null hypothesis is true (i.e., men and women are chosen with equal probability), the test statistic will be drawn from a chisquared distribution with one degree of freedom (because if the male frequency is known, then the female frequency is determined).
Consultation of the chisquared 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. 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 (i.e., we would consider our sample within the range of what we'd expect for a 50/50 male/female ratio.)
The approximation to the chisquared distribution breaks down if expected frequencies are too low. It will normally be acceptable so long as no more than 20% of the events have expected frequencies below 5. Where there is only 1 degree of freedom, the approximation is not reliable if expected frequencies are below 10. In this case, a better approximation can be obtained by reducing the absolute value of each difference between observed and expected frequencies by 0.5 before squaring; this is called Yates's correction for continuity.
In cases where the expected value, E, is found to be small (indicating a small underlying population probability, and/or a small number of observations), the normal approximation of the multinomial distribution can fail, and in such cases it is found to be more appropriate to use the Gtest, a likelihood ratiobased test statistic. When the total sample size is small, it is necessary to use an appropriate exact test, typically either the binomial test or (for contingency tables) Fisher's exact test. This test uses the conditional distribution of the test statistic given the marginal totals; however, it does not assume that the data were generated from an experiment in which the marginal totals are fixed and is valid whether or not that is the case.
