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statsmodels.stats.power.FTestPower.power

FTestPower.power(effect_size, df_num, df_denom, alpha, ncc=1)[source]

Calculate the power of a F-test.

Parameters:

effect_size : float

standardized effect size, mean divided by the standard deviation. effect size has to be positive.

df_num : int or float

numerator degrees of freedom.

df_denom : int or float

denominator degrees of freedom.

alpha : float in interval (0,1)

significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true.

ncc : int

degrees of freedom correction for non-centrality parameter. see Notes

Returns:

power : float

Power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true.

Notes

sample size is given implicitly by df_num

set ncc=0 to match t-test, or f-test in LikelihoodModelResults. ncc=1 matches the non-centrality parameter in R::pwr::pwr.f2.test

ftest_power with ncc=0 should also be correct for f_test in regression models, with df_num and d_denom as defined there. (not verified yet)

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