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--- r:chi-square-test [2020/04/21 17:08] – created astefanowitsch
+++ r:chi-square-test [2024/06/20 13:53] (current) – external edit 127.0.0.1
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+**[ [[R:introduction|Collection: Introduction to R]] ]**
+====== The Chi-Square test ======
+===== Prerequisites =====
+  * Your data must have the form of a contingency table (see [[R:contingency-tables|Contingency Tables]]).
+===== How to calculate the chi-square test =====
+Assuming that you have a contingency table that is stored in a variable called ''mytable'', you calculate a chi-square test using the ''chisq.test'' command as follows:
+	chisq.test(mytable, correct = FALSE)
+The option ''correct = FALSE'' ensures that the standard chi-square test is calculated, i.e., that no corrections are applied. If more than a quarter of the cells in your table have an expected value smaller than 5, you should use Yates' continuity correction; you can do this by not adding the ''correct'' option at all (i.e., R applies the correction by default), or by using the option ''correct = TRUE''.
+By default, R does not show the expected frequencies or the residuals, but they are created as internal variables by the ''chi.square()'' function internally, and you can force R to output them by attaching the name of the internal variable to the function, separated by a ''$'' sign:
+To show the expected frequencies, attach the option expected to the end of the command, separated by a $ sign:
+	chisq.test(mytable, correct = FALSE)$expected
+or
+	chisq.test(mytable, correct = FALSE)$residuals
+===== Additional information =====
+  * When you report the result of a chi-square test, you should include a) the chi-square value, b) the degrees of freedom, and c) the p-value
+  * If you want to present your data visually, a bar plot is usually the right way of doing so (see [[R:bar-plots|Bar Plots]]).