Fisher's exact

A 2x2 table with small counts.

When should I use Fisher's exact?

Fisher's exact is what Chavery runs when Chi-square does not hold: 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 the check fails, the parametric result would overstate what the data supports, so the engine hands over to this test rather than running the other one anyway.

What your data needs to look like

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

How researchers arrive at Fisher's exact

Most people reach this test by way of another one. Chavery routes here when an assumption of the parametric test does not hold:

• Chi-square — when expected cell counts fails (Expected cell counts).

That decision is made from your data, not from a preference. It is the same rule every time, which is why two people with the same numbers get the same test.

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

• Chi-square — Is a category associated with an outcome?
• McNemar — Paired yes/no answers, before and after.