Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.

Spearman's test calculates correlation by using ranks instead of raw data values, which makes it more flexible and robust than Pearson's. Unlike Pearson's correlation, which only measures linear relationships and assumes normally distributed data, Spearman's correlation can detect both linear and non-linear monotonic associations. It is also suitable for both continuous and discrete ordinal data, making it applicable in a wider range of scenarios where traditional parametric assumptions may not be met.

Spearman's rho ranges from -1 to +1, where the sign indicates the direction of the relationship: a negative sign shows an inverse correlation and a positive sign shows a direct correlation. When ranks are distinct, Spearman's rho is calculated using the formula:

Where d is the difference between the ranks for the two variables within a pair, and n is the sample size (total number of sample data pairs). To perform the test, the statistic rs is compared with the critical values at a specific significance level (usually at α= 0.05). These critical values are obtained from the standard table when the sample size is below 30 (i.e., n ≤ 30).

Spearman's rank correlation test is one of the most widely used correlation methods due to its robustness and versatility. It has an efficiency rating of approximately 0.91 when compared to Pearson's correlation coefficient, assuming all parametric requirements are met. This rating implies that Spearman's rho achieves comparable results to Pearson's correlation; for instance, using Spearman's rho with 100 data pairs can yield results similar to those from Pearson's correlation with 91 pairs. However, this efficiency measure does not mean that Spearman's test is only 91% accurate or correct only 91% of the time. Instead, it reflects the relative effectiveness of Spearman's test in capturing correlation strength compared to its parametric counterpart.