statsmodels.stats.power.GofChisquarePower.solve_power¶

GofChisquarePower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, n_bins=2)[source]¶

solve for any one parameter of the power of a one sample chisquare-test

for the one sample chisquare-test the keywords are:

effect_size, nobs, alpha, power

Exactly one needs to be None, all others need numeric values.

n_bins needs to be defined, a default=2 is used.

Parameters:¶

effect_size : float¶

standardized effect size, according to Cohen’s definition. see statsmodels.stats.gof.chisquare_effectsize

nobs : int or float¶

sample size, number of observations.

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.

power : float in interval (0,1)¶

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.

n_bins : int¶

number of bins or cells in the distribution

Returns:¶

value – The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters.

Return type:¶

float

Notes

The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses brentq with a prior search for bounds. If this fails to find a root, fsolve is used. If fsolve also fails, then, for alpha, power and effect_size, brentq with fixed bounds is used. However, there can still be cases where this fails.