Solved – Is it a reasonable rule of thumb to examine Pearson and Spearman correlations, and use the latter if they are very different

Question

The data I am using is continuous, and may be somewhat skewed, but probably will not have many outliers.

The following rule of thumb has been suggested to me: examine both Pearson and Spearman correlations, and if they are different to report the latter, on the grounds that a difference between the two might indicate that the assumptions of significance testing on Pearson's r are not met.

Answer 1

Pearson and Spearman correlation coefficients do not have the same meaning. Pearson gives information on the linear dependency between the two variables while Spearman give information on the generic relationship between the two variables.

For example in R,

x = rnorm(1000) # simulate 1000 samples of a standard Gaussian variable
cor(x, exp(100 * x), method = "spearman") # calculate Spearman correlation
# [1] 1 
cor(x, exp(100 * x), method = "pearson") # calculate Pearson correlation
# [1] 0.101593

You should report the correlation coefficient that makes more sense for your needs.