Question

1. In your own words, what is a nonparametric test? What is a parametric test?

2. In your own words, identify an advantage of using rank correlation instead of linear correlation

Answer 1

Non parametric test

A non parametric test does not assume anything about the underlying distribution (for example, that the data comes from a normal distribution). That’s compared to parametric test, which makes assumptions about a population’s parameters (for example, the mean or standard deviation); When the word “non parametric” is used in stats, it doesn’t quite mean that you know nothing about the population. It usually means that you know the population data does not have a normal distribution.

Parametric test

A parameter in statistics refers to an aspect of a population, as opposed to a statistic, which refers to an aspect about a sample. For example, the population mean is a parameter, while the sample mean is a statistic. A parametric statistical test makes an assumption about the population parameters and the distributions that the data came from. These types of test includes Student’s T tests and ANOVA tests, which assume data is from a normal distribution.

Advantage of rank correlation instead of linear correlation

An advantage of the Spearman rank correlation coefficient is that the X and Y values can be continuous or ordinal, and approximate normal distributions for X and Y are not required

Sometimes there doesn’t exist a marked linear relationship between two random variables but a monotonic relation (if one increases, the other also increases or instead, decreases) is clearly noticed. Pearson’s Correlation Coefficient evaluation, in this case, would give us the strength and direction of the linear association only between the variables of interest. Herein comes the advantage of the Spearman Rank Correlation methods, which will instead, give us the strength and direction of the monotonic relation between the connected variables