## What is the difference between Pearson and Kendall correlation?

we can see pearson and spearman are roughly the same, but kendall is very much different. That’s because Kendall is a test of strength of dependece (i.e. one could be written as a linear function of the other), whereas Pearson and Spearman are nearly equivalent in the way they correlate normally distributed data.

**Should I use Kendall’s tau or Spearman’s rho?**

In favor of Kendall’s tau: Spearman’s rho is more sensitive to error and discrepancies in the data. When data is normal, Kendall’s tau has smaller gross error sensitivity and smaller asymptotic variance.

**What does Kendall’s tau tell us?**

Kendall’s Tau is used to understand the strength of the relationship between two variables. Your variables of interest can be continuous or ordinal and should have a monotonic relationship. See more below. Kendall’s Tau is also called Kendall rank correlation coefficient, and Kendall’s tau-b.

### What is the difference between the Pearson correlation and the Spearman correlation?

The Pearson correlation evaluates the linear relationship between two continuous variables. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Spearman correlation is often used to evaluate relationships involving ordinal variables.

**How important is Kendall correlation coefficient in statistics?**

Here’s why: Kendall’s rank correlation measures the strength and direction of association that exists (determines if there’s a monotonic relationship) between two variables. Knowing this, testing for the presence of a monotonic relationship makes sense. But, like I said, it is desirable.

**What is a good Kendall’s tau?**

Kendall’s tau-B values: + or -0.10 to 0.19: weak. + or – 0.20 to 0.29: moderate. + or – 0.30 or above: strong.

## When should I use Pearson correlation?

Pearson’s correlation should be used only when there is a linear relationship between variables. It can be a positive or negative relationship, as long as it is significant. Correlation is used for testing in Within Groups studies.

**What is Tau-b in Kendall’s test?**

Kendall’s tau-b: This is Kendall’s correlation coefficient between the two variables. We typically use this value instead of tau-a because tau-b makes adjustments for ties. In this case, tau-b = -0.1752, indicating a negative correlation between the two variables. Prob > |z|: This is the p-value associated with the hypothesis test.

**What is the difference between Pearson and Spearman and Kendall’s test?**

That’s because Kendall is a test of strength of dependece (i.e. one could be written as a linear function of the other), whereas Pearson and Spearman are nearly equivalent in the way they correlate normally distributed data.

### Why can’t I use Pearson correlation to see if there is linear?

It’s not that you can’t use pearson to see if there is a linear relationship in data, it’s just that there are other tests suited to analyzing those different data structures. Outliers in your data can really throw off a Pearson correlation. More information on that here: http://www.purplemath.com/modules/boxwhisk3.htm

**Is Spearman more reliable than Pearson?**

Though pearson and spearman may be close to one another, spearman is reliable in this case because the data is not normally distributed. Again, you can still do a pearson correlation on non-normal data, but it’s not going to be as relaible as a non-parametric test which does not assume normality.