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statsmodels.stats.power.zt_ind_solve_power

statsmodels.stats.power.zt_ind_solve_power = <bound method NormalIndPower.solve_power of <statsmodels.stats.power.NormalIndPower object at 0x2ac65462d5d0>>

solve for any one parameter of the power of a two sample z-test

for z-test the keywords are:
effect_size, nobs1, alpha, power, ratio

exactly one needs to be None, all others need numeric values

Parameters:

effect_size : float

standardized effect size, difference between the two means divided by the standard deviation. If ratio=0, then this is the standardized mean in the one sample test.

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 ratio can be set to zero in order to get the power for a one sample test.

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 ration given the other arguments it has to be explicitly set to None.

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’.

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. If fsolve also fails, then, for alpha, power and effect_size, brentq with fixed bounds is used. However, there can still be cases where this fails.

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