bootstrapping correlations Spearman vs. Pearson

I would like to calculate simple correlations. Since my sample size is quite small and data are not normally distributed I thought about Spearman’s correlations. Then I read about bootstrapping as a distribution free method and, considering my small sample size, I decided to use it with Pearson’s correlation coefficient. However, recently I’ve found bootstrapping for Spearman correlations and got confused. If bootstrapping solves the problem with the data distribution and small sample size (i.e. one can use Pearson’s method) why I should bother with bootstrapped Spearman. Where does the problem lie? What method should I use? Are there any rules? Thanks!


Less is more. Stay pure. Stay poor.
Not a wizard in the area, but to my knowledge you typically conduct your test and get the 95 percentile interval for confidence of the measure from the bootstrap. So if you conducted the Pearson, would you think your main parameter was appropriate? Or would it be better to run a Spearman for parameter and then get the confidence interval from bootstrapping it?