- If there is an apparent relationship between two variables but the data does not show a normal distribution, Pearson’s linear correlation coefficient should not be used
- Spearman’s rank correlation determines whether there is correlation between variables that don’t show a normal distribution
- Step 1: Create a scatter graph and identify possible linear correlation
- Step 2: State a null hypothesis
- Step 3: Use the following equation to work out Spearman’s rank correlation coefficient r
- rs = spearman’s rank coefficient
- D = difference in rank
- n = number of samples
- Step 4: Refer to a table that relates critical values of rs to levels of probability
- If the value calculated for Spearman’s rank is greater than the critical value for the number of samples in the data ( n ) at the 0.05 probability level (p), then the null hypothesis can be rejected, meaning there is a correlation between two variables
You will be provided with the formula for Spearman’s rank correlation in the exam. You need to be able to carry out the calculation to test for correlation, as you could be asked to do this in the exam. You should understand when it is appropriate to use the different statistical tests that crop up in this topic, and the conditions in which each is valid.
Correlation does not always mean causation. Just because there is a correlation between the abundance of species A and species B it does not mean that the presence of species A causes the presence of species B.