Spearman correlation

Rank correlation, for ordinal or skewed data.

When should I use Spearman correlation?

At least one variable is a ranked category, so Spearman's correlation, which works on ranks rather than raw values, is appropriate.

What your data needs to look like

Two sets of measurements.

How researchers arrive at Spearman correlation

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

• Pearson correlation — when normality fails (Shapiro-Wilk).

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 offers instead, and when

• Pearson correlation — a distribution-free alternative when the assumptions are doubtful.

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

• Pearson correlation — Do two measurements move together?