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This might fit in xskillscore or climpred, but a useful calculation is the critical correlation coefficient (r) value for a Pearson correlation. This is the value that needs to be reached for statistical significance at a given alpha. It is computed by inverting the t statistic formula:
A straight-forward approach:
fromscipy.statsimporttp=0.05# two-tailed significance valuep=p/2# formula here assuming one-taileddf=60tval=t.ppf(p, df)
r_crit=tval/ (np.sqrt(df+tval**2))
This might fit in
xskillscore
orclimpred
, but a useful calculation is the critical correlation coefficient (r) value for a Pearson correlation. This is the value that needs to be reached for statistical significance at a given alpha. It is computed by inverting the t statistic formula:A straight-forward approach:
This aligns with values reported in various tables:
https://www.radford.edu/~jaspelme/statsbook/Chapter%20files/Table_of_Critical_Values_for_r.pdf
http://www.mun.ca/biology/scarr/Critical_Values_of_r.htm
https://www.statisticssolutions.com/table-of-critical-values-pearson-correlation/
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