# statsmodels.stats.power.FTestPower.solve_power¶

`FTestPower.``solve_power`(effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1)[source]

solve for any one parameter of the power of a F-test

for the one sample F-test the keywords are:
effect_size, df_num, df_denom, alpha, power

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

Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. 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. alternative : string, ‘two-sided’ (default) or ‘one-sided’ extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. ‘one-sided’ assumes we are in the relevant tail. value : float The value of the parameter that was set to None in the call. The value solves the power equation given the remainding parameters.

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.