A chi-square goodness-of-fit test is a statistical test used to determine whether the observed frequency distribution of a categorical variable matches an expected distribution. It compares the observed counts in each category to the counts that would be expected if the data followed a specific hypothesized distribution. If the difference between observed and expected frequencies is large enough, the null hypothesis (which assumes the data fits the expected distribution) is rejected, indicating that the data does not fit the expected distribution.