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How does one convert a Z-score from the Z-distribution (standard normal distribution, Gaussian distribution) to a p-value? I have yet to find the magical function in Scipy's stats module to do this, but one must be there.
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We use scipy. stats. norm. sf() function for calculating p-value from z-score.
z = (x – μ) / σ Assuming a normal distribution, your z score would be: z = (x – μ) / σ
Test with two tails In a two-tailed hypothesis test, let's say we wish to calculate the p-value associated with a z-score of 1.24. We simply multiplied the one-tailed p-value by two to get the two-tailed p-value. 0.2149 is the p-value.
I like the survival function (upper tail probability) of the normal distribution a bit better, because the function name is more informative:
p_values = scipy.stats.norm.sf(abs(z_scores)) #one-sided p_values = scipy.stats.norm.sf(abs(z_scores))*2 #twosided normal distribution "norm" is one of around 90 distributions in scipy.stats
norm.sf also calls the corresponding function in scipy.special as in gotgenes example
small advantage of survival function, sf: numerical precision should better for quantiles close to 1 than using the cdf
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