statsmodels.stats.power.TTestIndPower.solve_power¶

TTestIndPower.
solve_power
(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='twosided')[source]¶ solve for any one parameter of the power of a two sample ttest
 for ttest the keywords are:
 effect_size, nobs1, alpha, power, ratio
exactly one needs to be
None
, all others need numeric valuesParameters:  effect_size (float) – standardized effect size, difference between the two means divided by the standard deviation. effect_size has to be positive.
 nobs1 (int or float) – number of observations of sample 1. The number of observations of
sample two is ratio times the size of sample 1,
i.e.
nobs2 = nobs1 * ratio
 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.
 ratio (float) – ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ratio given the other arguments it has to be explicitly set to None.
 alternative (string, 'twosided' (default), 'larger', 'smaller') – extra argument to choose whether the power is calculated for a twosided (default) or one sided test. The onesided test can be either ‘larger’, ‘smaller’.
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. Iffsolve
also fails, then, foralpha
,power
andeffect_size
,brentq
with fixed bounds is used. However, there can still be cases where this fails.