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

  • 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.
  • 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 (int or float) – 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 (string, '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’.

power – 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.

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