What is the difference between pearson and spearmans correlation

Nakx 4 4 silver badges 19 19 bronze badges. Bonoboticians Bonoboticians 1, 1 1 gold badge 9 9 silver badges 2 2 bronze badges. Also, see this question: stats. I just checked the correlations in Anscombe's Quartet, and your link was helpful. Rather than examining each variable to see whether the assumptions of Pearson or Spearman correlation are met, just run both on everything. In many practical applications, they will give similar measures of significance of association, so you only need to dig deeper on the relatively few instances where their results differ greatly, and those are the interesting cases to learn more about anyway.

Show 1 more comment. Nick Cox A high apprehension has no definite difference with a very high apprehension, right? But I've seen that the variable has been correlated with other variables using Pearson's r. Is that totally okay? Thank you!

In case below, the two methods report an exactly opposite correlation. Some quick rules of thumb to decide on Spearman vs. Pearson: The assumptions of Pearson's are constant variance and linearity or something reasonably close to that , and if these are not met, it might be worth trying Spearman's.

If you feel that linear regression is a suitable method to analyze your data, then the output of Pearson's will match the sign and magnitude of a linear regression slope if the variables are standardized.

If your data has some non-linear components that linear regression won't pick up, then first try to straighten out the data into a linear form by applying a transform perhaps log e. If that doesn't work, then Spearman may be appropriate. I always try Pearson's first, and if that doesn't work, then I try Spearman's. Can you add any more rules of thumb or correct the ones I have just deduced?

I have made this question a community Wiki so you can do so. Here is the R code to reproduce the graph above: Script that shows that in some corner cases, the reported correlation for spearman can be exactly opposite to that for pearson. One assumption of regression is that the residuals are normally distributed. How would you check that before running the regression?

It is always a good idea to examine the relationship between variables with a scatterplot. Correlation coefficients only measure linear Pearson or monotonic Spearman relationships.

Other relationships are possible. However, the real value of correlation values is in quantifying less than perfect relationships. Finding that two variables are correlated often informs a regression analysis which tries to describe this type of relationship more.

This graph shows a very strong relationship. The Pearson coefficient and Spearman coefficient are both approximately 0. A comparison of the Pearson and Spearman correlation methods Learn more about Minitab.

In This Topic What is correlation? Comparison of Pearson and Spearman coefficients Other nonlinear relationships. As you drag the dots into more arrangements, you should see that the dots take up a roughly diagonal line, showing that although Pearson and Spearman coefficients are not the same, they are closely related.

Pearson's coefficient is. Spearman coefficient is. Click and drag the dots vertically Can you manipulate the dots so that both coefficients are close to minus one? Can you manipulate the dots so that both coefficients are close to minus one? What happens to the coefficients when you create this shape? What the correlation of a "smile" curve? You should find that both coefficients are near zero.