pingouin.power_ttest2n

pingouin.
power_ttest2n
(nx, ny, d=None, power=None, alpha=0.05, tail='twosided')[source] Evaluate power, effect size or significance level of an independent twosamples Ttest with unequal sample sizes.
 Parameters
 nx, nyint
Sample sizes. Must be specified. If the sample sizes are equal, you should use the
power_ttest()
function instead. dfloat
Cohen d effect size
 powerfloat
Test power (= 1  type II error).
 alphafloat
Significance level (type I error probability). The default is 0.05.
 tailstr
Indicates the alternative of the test. Can be either ‘twosided’, ‘greater’ or ‘less’.
Notes
Exactly ONE of the parameters
d
,power
andalpha
must be passed as None, and that parameter is determined from the others.Notice that
alpha
has a default value of 0.05 so None must be explicitly passed if you want to compute it.This function is a Python adaptation of the pwr.t2n.test function implemented in the pwr R package.
Statistical power is the likelihood that a study will detect an effect when there is an effect there to be detected. A high statistical power means that there is a low probability of concluding that there is no effect when there is one. Statistical power is mainly affected by the effect size and the sample size.
The first step is to use the Cohen’s d to calculate the noncentrality parameter \(\delta\) and degrees of freedom \(v\). In case of two independent groups with unequal sample sizes, this is:
\[\delta = d * \sqrt{\frac{n_i * n_j}{n_i + n_j}}\]\[v = n_i + n_j  2\]where \(d\) is the Cohen d, \(n\) the sample size, \(n_i\) the sample size of the first group and \(n_j\) the sample size of the second group,
The critical value is then found using the percent point function of the T distribution with \(q = 1  alpha\) and \(v\) degrees of freedom.
Finally, the power of the test is given by the survival function of the noncentral distribution using the previously calculated critical value, degrees of freedom and noncentrality parameter.
scipy.optimize.brenth()
is used to solve power equations for other variables (i.e. sample size, effect size, or significance level). If the solving fails, a nan value is returned.Results have been tested against GPower and the pwr R package.
Examples
Compute achieved power of a Ttest given
d
,n
andalpha
>>> from pingouin import power_ttest2n >>> print('power: %.4f' % power_ttest2n(nx=20, ny=15, d=0.5, ... tail='greater')) power: 0.4164
Compute achieved
d
givenn
,power
andalpha
level
>>> print('d: %.4f' % power_ttest2n(nx=20, ny=15, power=0.80, alpha=0.05)) d: 0.9859
Compute achieved alpha level given
d
,n
andpower
>>> print('alpha: %.4f' % power_ttest2n(nx=20, ny=15, d=0.5, ... power=0.80, alpha=None)) alpha: 0.5000