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
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How would you determine your sample size for this test?
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