Chi-square Test
A test for categorical data that compares observed frequencies with those expected under the null hypothesis, often to check whether two variables are related.
The chi-square test analyzes categorical variables by comparing the frequencies you observed with the frequencies expected if the null hypothesis were true. The test of independence asks whether two categorical variables, arranged in a contingency table, are associated; the goodness-of-fit test asks whether one variable's distribution matches a specified pattern. Larger gaps between observed and expected counts yield a larger chi-square statistic and a smaller p-value.
The test assumes independent observations and adequate expected counts in each cell, commonly at least five; with very small samples an exact test is preferable. It shows association, not causation or direction.
In a thesis, chi-square tests suit survey data and demographic comparisons, such as whether completion rates differ across enrollment categories. Report the contingency table, the statistic with its degrees of freedom, the p-value, and a measure of association so readers can judge the strength of the relationship.
Writing the thesis this term belongs to?
CiteDash takes a thesis from research question to a compiled document, with AI that cites only real papers and verifies every claim against its source.