/Statsmodels

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