statsmodels.stats.power.tt_solve_power¶
- statsmodels.stats.power.tt_solve_power(effect_size=None, nobs=None, alpha=None, power=None, alternative='two-sided')¶
solve for any one parameter of the power of a one sample t-test
- for the one sample t-test the keywords are:
effect_size, nobs, alpha, power
Exactly one needs to be
None
, all others need numeric values.This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of pairs.
- Parameters:
- effect_size
float
standardized effect size, mean divided by the standard deviation. effect size has to be positive.
- nobs
int
orfloat
sample size, number of observations.
- 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.
- alternative
str
, ‘two-sided’ (default
)or
‘one-sided’ extra argument to choose whether the power is calculated for a two-sided (default) or one sided test. ‘one-sided’ assumes we are in the relevant tail.
- effect_size
- 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.
- attaches
- cache_fit_res
list
Cache of the result of the root finding procedure for the latest call to
solve_power
, mainly for debugging purposes. The first element is the success indicator, one if successful. The remaining elements contain the return information of the up to three solvers that have been tried.
- value
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.