solve_power(effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1)¶
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.
standardized effect size, mean divided by the standard deviation. effect size has to be positive.
sample size, number of observations.
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 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.
str, ‘two-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.
The value of the parameter that was set to None in the call. The value solves the power equation given the remaining parameters.
The function uses scipy.optimize for finding the value that satisfies the power equation. It first uses
brentqwith a prior search for bounds. If this fails to find a root,
fsolveis used. If
fsolvealso fails, then, for
brentqwith fixed bounds is used. However, there can still be cases where this fails.