pystatpower.mean.independent.ci ¶

FUNCTION	DESCRIPTION
`solve_precision`	Calculate the distance from the mean difference to the confidence limit.
`solve_size`	Estimate the required sample size.

solve_precision ¶

solve_precision(
    *,
    treatment_std: float,
    reference_std: float,
    treatment_size: int,
    reference_size: int,
    conf_level: float = 0.95,
    interval_type: Literal["two-sided", "lower", "upper"] = "two-sided",
    equal_var: bool = False,
) -> float

Calculate the distance from the mean difference to the confidence limit.

PARAMETER	DESCRIPTION
`treatment_std`	Standard deviation in the treatment group. TYPE: `float`
`reference_std`	Standard deviation in the reference group. TYPE: `float`
`treatment_size`	Sample size in the treatment group. TYPE: `int`
`reference_size`	Sample size in the reference group. TYPE: `int`
`conf_level`	Confidence level. TYPE: `float` DEFAULT: `0.95`
`interval_type`	The type of confidence interval. `'two-sided'`: Two-sided confidence interval. `'lower'`: Lower one-sided confidence interval. `'upper'`: Upper one-sided confidence interval. TYPE: `Literal['two-sided', 'lower', 'upper']` DEFAULT: `'two-sided'`
`equal_var`	Specify whether the variances are assumed to be equal. `True`: The variances are assumed to be equal. In this case, the standard t-test is used. `False`: The variances are not assumed to be equal. In this case, Welch-Satterthwaite's approximate t-test is used. TYPE: `bool` DEFAULT: `False`

RETURNS	DESCRIPTION
`float`	The distance from the mean difference to the confidence limit.

solve_size ¶

solve_size(
    *,
    treatment_std: float,
    reference_std: float,
    precision: float,
    ratio: float = 1,
    conf_level: float = 0.95,
    interval_type: Literal["two-sided", "lower", "upper"] = "two-sided",
    equal_var: bool = False,
) -> tuple[int, int]

Estimate the required sample size.

PARAMETER	DESCRIPTION
`treatment_std`	Standard deviation in the treatment group. TYPE: `float`
`reference_std`	Standard deviation in the reference group. TYPE: `float`
`precision`	The distance from the mean difference to the confidence limit. TYPE: `float`
`ratio`	The ratio of the sample size in the treatment group to the sample size in the reference group. TYPE: `float` DEFAULT: `1`
`conf_level`	Confidence level. TYPE: `float` DEFAULT: `0.95`
`interval_type`	The type of confidence interval. `'two-sided'`: Two-sided confidence interval. `'lower'`: Lower one-sided confidence interval. `'upper'`: Upper one-sided confidence interval. TYPE: `Literal['two-sided', 'lower', 'upper']` DEFAULT: `'two-sided'`
`equal_var`	Specify whether the variances are assumed to be equal. `True`: The variances are assumed to be equal. In this case, the standard t-test is used. `False`: The variances are not assumed to be equal. In this case, Welch-Satterthwaite's approximate t-test is used. TYPE: `bool` DEFAULT: `False`

RETURNS	DESCRIPTION
`tuple[int, int]`	The required sample sizes in the treatment and reference groups, respectively.