pystatpower.mean.independent.ci ¶

Functions:

Name	Description
`solve_precision`	Calculate the distance from the mean difference to the confidence limit.
`solve_size`	Estimate the required sample size, given the distance from the mean difference to the confidence limit.

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.

Parameters:

Name	Type	Description	Default
`treatment_std`	`float`	Standard deviation in the treatment group.	required
`reference_std`	`float`	Standard deviation in the reference group.	required
`treatment_size`	`int`	Sample size in the treatment group.	required
`reference_size`	`int`	Sample size in the reference group.	required
`conf_level`	`float`	Confidence level.	`0.95`
`interval_type`	`Literal['two-sided', 'lower', 'upper']`	The type of confidence interval. `'two-sided'`: Two-sided confidence interval. `'lower'`: Lower one-sided confidence interval. `'upper'`: Upper one-sided confidence interval.	`'two-sided'`
`equal_var`	`bool`	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.	`False`

Returns:

Name	Type	Description
`float`	`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, given the distance from the mean difference to the confidence limit.

Parameters:

Name	Type	Description	Default
`treatment_std`	`float`	Standard deviation in the treatment group.	required
`reference_std`	`float`	Standard deviation in the reference group.	required
`precision`	`float`	The distance from the mean difference to the confidence limit.	required
`ratio`	`float`	The ratio of the sample size in the treatment group to the sample size in the reference group.	`1`
`conf_level`	`float`	Confidence level.	`0.95`
`interval_type`	`Literal['two-sided', 'lower', 'upper']`	The type of confidence interval. `'two-sided'`: Two-sided confidence interval. `'lower'`: Lower one-sided confidence interval. `'upper'`: Upper one-sided confidence interval.	`'two-sided'`
`equal_var`	`bool`	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.	`False`

Returns:

Type	Description
`tuple[int, int]`	tuple[int, int]: The required sample size in the treatment and reference groups, respectively.