statsmodels.stats.power.tt_ind_solve_power¶

statsmodels.stats.power.
tt_ind_solve_power
= <bound method TTestIndPower.solve_power of <statsmodels.stats.power.TTestIndPower object at 0x108a9efd0>>¶ 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 : float
The value of the parameter that was set to None in the call. The value solves the power equation given the remaining 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. Iffsolve
also fails, then, foralpha
,power
andeffect_size
,brentq
with fixed bounds is used. However, there can still be cases where this fails.