statsmodels.stats.power.TTestIndPower.power¶

TTestIndPower.power(effect_size, nobs1, alpha, ratio=`1`, df=`None`, alternative=`'two-sided'`)[source]

Calculate the power of a t-test for two independent sample

Parameters:
effect_size`float`

standardized effect size, difference between the two means divided by the standard deviation. effect_size has to be positive.

nobs1

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.

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.

df

degrees of freedom. By default this is None, and the df from the ttest with pooled variance is used, `df = (nobs1 - 1 + nobs2 - 1)`

alternative`str`, ‘two-sided’ (`default`), ‘larger’, ‘smaller’

extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. The one-sided test can be either ‘larger’, ‘smaller’.

Returns:
power`float`

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.

Last update: Jul 16, 2024