• 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
• Method:
  • 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

• • • Where:
      • 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

As the?data was not normally distributed?they decided to use Spearman’s rank correlation coefficient.

Null hypothesis: there is no correlation between the abundance of species A and species B.

• • n = 10 as there are 10 quadrat samples

Step 1:?Rank each set of data (rank 1 being the smallest data figure)

Step 2:?Find the difference in rank between the two species,?D

Step 3:?Square the difference in rank,?D2?(= 6)

Step 4: Substitute the appropriate numbers into the equation (remember?n = 10)

Step 5:? Refer to a table that relates values of?rs?to probability. Look for the 0.05 probability level with n = 10

• • As Rs?= 0.964, it is greater than the critical value of 0.65. The null hypothesis can be rejected, there is genuine?positive correlation?between the abundance of species A and B