Chi-square

Is a category associated with an outcome?

When should I use Chi-square?

Each participant is counted once and the groups are separate, so you are comparing how counts are distributed across categories, which is what a chi-square test of independence assesses.

What your data needs to look like

A table of counts — how many people fall into each category, not percentages.

What Chi-square assumes

Chavery checks each of these against your data before it runs the test, and says whether it held. It does not ask you to assert them.

Expected cell counts (Expected cell counts)

Every cell of the table would be expected to hold at least five cases. Chi-square is an approximation that stops holding when the counts get small. When this one fails, Chavery runs Fisher's exact instead.

What Chavery offers instead, and when

• Fisher's exact — when expected cell counts does not hold.

What Chavery does with it

It checks the assumptions first and says whether each one held. When an assumption fails it names the alternative test rather than running this one anyway.

Then it reports the statistic, the p-value, a confidence interval and an effect size, and writes the sentence you would put in your results chapter. The same numbers always give the same answer; nothing is generated by a language model.

Reporting it

A p-value on its own is not a result. Chavery reports the effect size beside it, because a difference can be statistically significant and too small to matter, and an examiner will ask which yours is. More on effect size.

Not sure this is the test you need? Work through the question Chavery asks and it will name the test your design calls for.

Related tests

• Fisher's exact — A 2x2 table with small counts.
• McNemar — Paired yes/no answers, before and after.