Pearson's chi-squared test or Pearson's test 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 the most widely used of many chi-squared tests (e.g., Yates, likelihood ratio, portmanteau test in time series, etc.) – statistical procedures whose results are evaluated by reference to the chi-squared distribution. Its properties were first investigated by Karl Pearson in 1900. In contexts where it is important to improve a distinction between the test statistic and its distribution, names similar to Pearson χ-squared test or statistic are used. It is a p-value test.
A simple example is testing the hypothesis that an ordinary six-sided die is "fair" (i. e., all six outcomes are equally likely to occur). In this case, the observed data is , the number of times that the die has fallen on each number. The null hypothesis is , and . As detailed below, if , then the fairness of the die can be rejected at the level of .
Procedure The setup is as follows:
Before the experiment, the experimenter fixes a certain number of samples to take. The observed data is , the count number of samples from a finite set of given categories. They satisfy . The null hypothesis is that the count numbers are sampled from a multinomial distribution . That is, the underlying data is sampled IID from a categorical distribution over the given categories. The Pearson's chi-squared test statistic is defined as . The p-value of the test statistic is computed either numerically or by looking it up in a table. If the p-value is small enough (usually p < 0.05 by convention), then the null hypothesis is rejected, and we conclude that the observed data does not follow the multinomial distribution.
Usage Pearson's chi-squared test is used to assess three types of comparison: goodness of fit, homogeneity, and independence. A test of goodness of fit establishes whether an observed frequency distribution differs from a theoretical distribution. A test of homogeneity compares the distribution of counts for two or more groups using the same categorical variable (e.g. choice of activity—college, military, employment, travel—of graduates of a high school reported a year after graduation, sorted by graduation year, to see if number of graduates choosing a given activity has changed from class to class, or from decade to decade).David E. Bock, Paul F. Velleman, Richard D. De Veaux (2007). "Stats, Modeling the World," pp. 606-627, Pearson Addison Wesley, Boston, A test of independence assesses whether observations consisting of measures on two variables, expressed in a contingency table, are independent of each other (e.g. polling responses from people of different nationalities to see if one's nationality is related to the response).
For all three tests, the computational procedure includes the following steps: Calculate the chi-squared test statistic, , which resembles a normalized sum of squared deviations between observed and theoretical frequencies (see below). Determine the degrees of freedom, df, of that statistic. For a test of goodness-of-fit, , where Cats is the number of observation categories recognized by the model, and Params is the number of parameters in the model adjusted to make the model best fit the observations: The number of categories reduced by the number of fitted parameters in the distribution. For a test of homogeneity, , where Rows corresponds to the number of categories (i.e. rows in the associated contingency table), and Cols corresponds to the number of independent groups (i.e. columns in the associated contingency table). For a test of independence, , where in this case, Rows corresponds to the number of categories in one variable, and Cols corresponds to the number of categories in the second variable. Select a desired level of confidence (significance level, p-value, or the corresponding alpha level) for the result of the test. Compare to the critical value from the chi-squared distribution with df degrees of freedom and the selected confidence level (one-sided, since the test is only in one direction, i.e. is the test value greater than the critical value?), which in many cases gives a good approximation of the distribution of . Sustain or reject the null hypothesis that the observed frequency distribution is the same as the theoretical distribution based on whether the test statistic exceeds the critical value of . If the test statistic exceeds the critical value of , the null hypothesis ( = there is no difference between the distributions) can be rejected, and the alternative hypothesis ( = there is a difference between the distributions) can be accepted, both with the selected level of confidence. If the test statistic falls below the threshold value, then no clear conclusion can be reached, and the null hypothesis is sustained (we fail to reject the null hypothesis), though not necessarily accepted.
Test for fit of a distribution
Discrete uniform distribution
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 .
One specific example of its application would be its application for log-rank test.
Other distributions
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 parameters used in fitting the distribution. For instance, when checking a three-parameter Generalized gamma distribution, , and when checking a normal distribution (where the parameters are mean and standard deviation), , and when checking a Poisson distribution (where the parameter is the expected value), . Thus, there will be degrees of freedom, where is the number of categories.
The degrees of freedom are not based on the number of observations as with a Student's t or F-distribution. For example, if testing for a fair, six-sided die, there would be five degrees of freedom because there are six categories or parameters (each number); the number of times the die is rolled does not influence the number of degrees of freedom.
Calculating the test-statistic
+ Upper-tail critical values of chi-square distributionDegrees offreedomProbability less than the critical value 0.90 0.95 0.975 0.99 0.999 1 2.706 3.841 5.024 6.635 10.828 2 4.605 5.991 7.378 9.210 13.816 3 6.251 7.815 9.348 11.345 16.266 4 7.779 9.488 11.143 13.277 18.467 5 9.236 11.070 12.833 15.086 20.515 6 10.645 12.592 14.449 16.812 22.458 7 12.017 14.067 16.013 18.475 24.322 8 13.362 15.507 17.535 20.090 26.125 9 14.684 16.919 19.023 21.666 27.877 10 15.987 18.307 20.483 23.209 29.588 11 17.275 19.675 21.920 24.725 31.264 12 18.549 21.026 23.337 26.217 32.910 13 19.812 22.362 24.736 27.688 34.528 14 21.064 23.685 26.119 29.141 36.123 15 22.307 24.996 27.488 30.578 37.697 16 23.542 26.296 28.845 32.000 39.252 17 24.769 27.587 30.191 33.409 40.790 18 25.989 28.869 31.526 34.805 42.312 19 27.204 30.144 32.852 36.191 43.820 20 28.412 31.410 34.170 37.566 45.315 21 29.615 32.671 35.479 38.932 46.797 22 30.813 33.924 36.781 40.289 48.268 23 32.007 35.172 38.076 41.638 49.728 24 33.196 36.415 39.364 42.980 51.179 25 34.382 37.652 40.646 44.314 52.620 26 35.563 38.885 41.923 45.642 54.052 27 36.741 40.113 43.195 46.963 55.476 28 37.916 41.337 44.461 48.278 56.892 29 39.087 42.557 45.722 49.588 58.301 30 40.256 43.773 46.979 50.892 59.703 31 41.422 44.985 48.232 52.191 61.098 32 42.585 46.194 49.480 53.486 62.487 33 43.745 47.400 50.725 54.776 63.870 34 44.903 48.602 51.966 56.061 65.247 35 46.059 49.802 53.203 57.342 66.619 36 47.212 50.998 54.437 58.619 67.985 37 48.363 52.192 55.668 59.893 69.347 38 49.513 53.384 56.896 61.162 70.703 39 50.660 54.572 58.120 62.428 72.055 40 51.805 55.758 59.342 63.691 73.402 41 52.949 56.942 60.561 64.950 74.745 42 54.090 58.124 61.777 66.206 76.084 43 55.230 59.304 62.990 67.459 77.419 44 56.369 60.481 64.201 68.710 78.750 45 57.505 61.656 65.410 69.957 80.077 46 58.641 62.830 66.617 71.201 81.400 47 59.774 64.001 67.821 72.443 82.720 48 60.907 65.171 69.023 73.683 84.037 49 62.038 66.339 70.222 74.919 85.351 50 63.167 67.505 71.420 76.154 86.661 51 64.295 68.669 72.616 77.386 87.968 52 65.422 69.832 73.810 78.616 89.272 53 66.548 70.993 75.002 79.843 90.573 54 67.673 72.153 76.192 81.069 91.872 55 68.796 73.311 77.380 82.292 93.168 56 69.919 74.468 78.567 83.513 94.461 57 71.040 75.624 79.752 84.733 95.751 58 72.160 76.778 80.936 85.950 97.039 59 73.279 77.931 82.117 87.166 98.324 60 74.397 79.082 83.298 88.379 99.607 61 75.514 80.232 84.476 89.591 100.888 62 76.630 81.381 85.654 90.802 102.166 63 77.745 82.529 86.830 92.010 103.442 64 78.860 83.675 88.004 93.217 104.716 65 79.973 84.821 89.177 94.422 105.988 66 81.085 85.965 90.349 95.626 107.258 67 82.197 87.108 91.519 96.828 108.526 68 83.308 88.250 92.689 98.028 109.791 69 84.418 89.391 93.856 99.228 111.055 70 85.527 90.531 95.023 100.425 112.317 71 86.635 91.670 96.189 101.621 113.577 72 87.743 92.808 97.353 102.816 114.835 73 88.850 93.945 98.516 104.010 116.092 74 89.956 95.081 99.678 105.202 117.346 75 91.061 96.217 100.839 106.393 118.599 76 92.166 97.351 101.999 107.583 119.850 77 93.270 98.484 103.158 108.771 121.100 78 94.374 99.617 104.316 109.958 122.348 79 95.476 100.749 105.473 111.144 123.594 80 96.578 101.879 106.629 112.329 124.839 81 97.680 103.010 107.783 113.512 126.083 82 98.780 104.139 108.937 114.695 127.324 83 99.880 105.267 110.090 115.876 128.565 84 100.980 106.395 111.242 117.057 129.804 85 102.079 107.522 112.393 118.236 131.041 86 103.177 108.648 113.544 119.414 132.277 87 104.275 109.773 114.693 120.591 133.512 88 105.372 110.898 115.841 121.767 134.746 89 106.469 112.022 116.989 122.942 135.978 90 107.565 113.145 118.136 124.116 137.208 91 108.661 114.268 119.282 125.289 138.438 92 109.756 115.390 120.427 126.462 139.666 93 110.850 116.511 121.571 127.633 140.893 94 111.944 117.632 122.715 128.803 142.119 95 113.038 118.752 123.858 129.973 143.344 96 114.131 119.871 125.000 131.141 144.567 97 115.223 120.990 126.141 132.309 145.789 98 116.315 122.108 127.282 133.476 147.010 99 117.407 123.225 128.422 134.642 148.230 100 118.498 124.342 129.561 135.807 149.449 The value of the test-statistic is
where = Pearson's cumulative test statistic, which asymptotically approaches a distribution. = the number of observations of type i. = total number of observations = the expected (theoretical) count of type i, asserted by the null hypothesis that the fraction of type i in the population is = the number of cells in the table.
The chi-squared statistic can then be used to calculate a p-value by comparing the value of the statistic to a chi-squared distribution. The number of degrees of freedom is equal to the number of cells , minus the reduction in degrees of freedom, .
The chi-squared statistic can be also calculated as
This result is the consequence of the Binomial theorem.
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 chi-squared statistic. More generally however, when maximum likelihood estimation does not coincide with minimum chi-squared estimation, the distribution will lie somewhere between a chi-squared distribution with and degrees of freedom (See for instance Chernoff and Lehmann, 1954).