What is true about Spearman's correlation test?

Spearman's correlation test is a non-parametric measure of correlation that assesses how well the relationship between two variables can be described using a monotonic function. Unlike parametric tests, Spearman's correlation does not assume that the data is normally distributed or that the relationships between the variables are linear.

The test is particularly useful for ordinal data or for interval data that do not meet the assumptions required for parametric tests. It ranks the data points rather than relying on the actual values, making it a robust choice when the data may not meet the stricter assumptions of parametric testing.

Additionally, since it is designed to evaluate the strength and direction of the relationship based on ranked values, it effectively handles tied ranks and is more flexible with regards to different types of data distributions. This is why it is appropriate to categorize Spearman's correlation as a non-parametric test.