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It requires a large enough sample size for accuracy, cannot be used for small expected frequencies, and is not suitable for ordinal or continuous data without categorization.

A data table that displays frequencies for two categorical variables and is used in chi-square tests of independence.

It is used to determine whether there is a significant association between two categorical variables.

Results are reported with the chi-square statistic value, degrees of freedom, p-value, and possibly an effect size measure, alongside a conclusion regarding the null hypothesis.

The p-value assesses the evidence against the null hypothesis, with values less than the significance level indicating a rejection of the null hypothesis.

Used to explore which specific categories or cells contribute to the significance in the chi-square test, in the case of a significant overall result.

Data must be randomly sampled, observations should be independent of each other, and expected frequencies should be at least 5.

A statistical hypothesis test that measures how well observed data fits with the expected distribution of the data, assuming the null hypothesis is true.

The formula is

Expected frequencies are calculated based on the null hypothesis and the distribution of the sample data.

In chi-square tests, the null hypothesis asserts that there is no difference between the observed and expected frequencies, or no association between variables.

The distribution of the chi-square statistic under the null hypothesis, which is dependent on degrees of freedom.

It is used to determine whether sample data is consistent with a population with a specific distribution.

Degrees of freedom for the Chi-Square test is the number of categories minus one for goodness of fit, or (rows - 1) * (columns - 1) for a test of independence.