Rank Correlation Test: Using Spearman Rank Correlation Coefficient Nonparametric Test - Statistics

Spearman's Rank correlation coefficient is a technique which can be used to summarise the strength and direction (negative or positive) of a relationship between two variables. The result will always be between 1 and minus 1. Method - calculating the coefficient. Create a table from your data. Rank the two data sets.
The Pearson correlation evaluates the linear relationship between two continuous variables. A relationship is linear when a change in one variable is associated with a proportional change in the other variable. ... The Spearman correlation evaluates the monotonic relationship between two continuous or ordinal variables.

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[@mullany08uk]

How would you determine your sample size for this test?

[@quentingallea166]

Check "Sample Size Charts for Spearman and Kendall Coefficients" (May and Looney (2020)). For non parametric tests, it is common to compute the sample size using parametric corresponding test and then inflate slightly the results (as non parametric tests have usually lower power). You can use GPower (R or Python as well) to compute the power for a Pearson Exact test and then increase by approx 10% (this is a bit wild, refer rather to the first paper I mentionned