pystatpower.proportion.independent.inequality ¶

FUNCTION	DESCRIPTION
`solve_power`	Calculate the statistical power.
`solve_size`	Estimate the required sample size.
`solve_treatment_proportion`	Estimate the required proportion in the treatment group.
`solve_reference_proportion`	Estimate the required proportion in the reference group.

solve_power ¶

solve_power(
    *,
    treatment_proportion: float,
    reference_proportion: float,
    treatment_size: int,
    reference_size: int,
    alternative: Literal["two-sided", "one-sided"],
    alpha: float = 0.05,
    method: Literal["z-pooled", "z-unpooled"] = "z-unpooled",
    continuity_correction: bool = False,
) -> float

Calculate the statistical power.

PARAMETER	DESCRIPTION
`treatment_proportion`	Proportion in the treatment group. TYPE: `float`
`reference_proportion`	Proportion 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`
`alternative`	Type of the alternative hypothesis. If `alternative` is `'two-sided'`, the alternative hypothesis is \(p_1 ≠ p_2\) If `alternative` is `'one-sided'`, the alternative hypothesis is \(p_1 > p_2\) or \(p_1 < p_2\), depending on the value of `treatment_proportion` and `reference_proportion`. TYPE: `Literal['two-sided', 'one-sided']`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'one-sided'`, `alpha` represents the one-sided significance level. TYPE: `float` DEFAULT: `0.05`
`method`	The method used to construct the test statistic. `'z-pooled'`: Z-test using pooled variance. `'z-unpooled'`: Z-test using unpooled variance. TYPE: `Literal['z-pooled', 'z-unpooled']` DEFAULT: `'z-unpooled'`
`continuity_correction`	Wether to apply Yates' continuity correction. TYPE: `bool` DEFAULT: `False`

RETURNS	DESCRIPTION
`float`	The statistical power of the test.

solve_size ¶

solve_size(
    *,
    treatment_proportion: float,
    reference_proportion: float,
    alternative: Literal["two-sided", "one-sided"],
    ratio: float = 1,
    alpha: float = 0.05,
    power: float = 0.8,
    method: Literal["z-pooled", "z-unpooled"] = "z-unpooled",
    continuity_correction: bool = False,
) -> tuple[int, int]

Estimate the required sample size.

PARAMETER	DESCRIPTION
`treatment_proportion`	Proportion in the treatment group. TYPE: `float`
`reference_proportion`	Proportion in the reference group. TYPE: `float`
`alternative`	Type of the alternative hypothesis. If `alternative` is `'two-sided'`, the alternative hypothesis is \(p_1 ≠ p_2\) If `alternative` is `'one-sided'`, the alternative hypothesis is \(p_1 > p_2\) or \(p_1 < p_2\), depending on the value of `treatment_proportion` and `reference_proportion`. TYPE: `Literal['two-sided', 'one-sided']`
`ratio`	Ratio of sample sizes in the treatment and reference groups. TYPE: `float` DEFAULT: `1`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'one-sided'`, `alpha` represents the one-sided significance level. TYPE: `float` DEFAULT: `0.05`
`power`	Expected statistical power. 0.8 is a commonly used value for statistical power. TYPE: `float` DEFAULT: `0.8`
`method`	The method used to construct the test statistic. `'z-pooled'`: Z-test using pooled variance. `'z-unpooled'`: Z-test using unpooled variance. TYPE: `Literal['z-pooled', 'z-unpooled']` DEFAULT: `'z-unpooled'`
`continuity_correction`	Wether to apply Yates' continuity correction. TYPE: `bool` DEFAULT: `False`

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

solve_treatment_proportion ¶

solve_treatment_proportion(
    *,
    reference_proportion: float,
    treatment_size: int,
    reference_size: int,
    alternative: Literal["two-sided", "one-sided"],
    alpha: float = 0.05,
    power: float = 0.8,
    method: Literal["z-pooled", "z-unpooled"] = "z-unpooled",
    continuity_correction: bool = False,
    direction: Literal["greater", "less"],
) -> float

Estimate the required proportion in the treatment group.

PARAMETER	DESCRIPTION
`reference_proportion`	Proportion 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`
`alternative`	Type of the alternative hypothesis. If `alternative` is `'two-sided'`, the alternative hypothesis is \(p_1 ≠ p_2\) If `alternative` is `'one-sided'`, the alternative hypothesis is \(p_1 > p_2\) or \(p_1 < p_2\), depending on the value of `treatment_proportion` and `reference_proportion`. TYPE: `Literal['two-sided', 'one-sided']`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'one-sided'`, `alpha` represents the one-sided significance level. TYPE: `float` DEFAULT: `0.05`
`power`	Expected statistical power. 0.8 is a commonly used value for statistical power. TYPE: `float` DEFAULT: `0.8`
`method`	The method used to construct the test statistic. `'z-pooled'`: Z-test using pooled variance. `'z-unpooled'`: Z-test using unpooled variance. TYPE: `Literal['z-pooled', 'z-unpooled']` DEFAULT: `'z-unpooled'`
`continuity_correction`	Wether to apply Yates' continuity correction. TYPE: `bool` DEFAULT: `False`
`direction`	The direction for the treatment proportion relative to the reference proportion. `'greater'`: Search for the treatment proportion greater than the reference proportion. `'less'`: Search for the treatment proportion less than the reference proportion. TYPE: `Literal['greater', 'less']`

RETURNS	DESCRIPTION
`float`	The required proportion in the treatment group.

solve_reference_proportion ¶

solve_reference_proportion(
    *,
    treatment_proportion: float,
    treatment_size: int,
    reference_size: int,
    alternative: Literal["two-sided", "one-sided"],
    alpha: float = 0.05,
    power: float = 0.8,
    method: Literal["z-pooled", "z-unpooled"] = "z-unpooled",
    continuity_correction: bool = False,
    direction: Literal["greater", "less"],
) -> float

Estimate the required proportion in the reference group.

PARAMETER	DESCRIPTION
`treatment_proportion`	Proportion in the treatment group. TYPE: `float`
`treatment_size`	Sample size in the treatment group. TYPE: `int`
`reference_size`	Sample size in the reference group. TYPE: `int`
`alternative`	Type of the alternative hypothesis. If `alternative` is `'two-sided'`, the alternative hypothesis is \(p_1 ≠ p_2\) If `alternative` is `'one-sided'`, the alternative hypothesis is \(p_1 > p_2\) or \(p_1 < p_2\), depending on the value of `treatment_proportion` and `reference_proportion`. TYPE: `Literal['two-sided', 'one-sided']`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'one-sided'`, `alpha` represents the one-sided significance level. TYPE: `float` DEFAULT: `0.05`
`power`	Expected statistical power. 0.8 is a commonly used value for statistical power. TYPE: `float` DEFAULT: `0.8`
`method`	The method used to construct the test statistic. `'z-pooled'`: Z-test using pooled variance. `'z-unpooled'`: Z-test using unpooled variance. TYPE: `Literal['z-pooled', 'z-unpooled']` DEFAULT: `'z-unpooled'`
`continuity_correction`	Wether to apply Yates' continuity correction. TYPE: `bool` DEFAULT: `False`
`direction`	The direction for the treatment proportion relative to the reference proportion. `'greater'`: Search for the treatment proportion greater than the reference proportion. `'less'`: Search for the treatment proportion less than the reference proportion. TYPE: `Literal['greater', 'less']`

RETURNS	DESCRIPTION
`float`	The required proportion in the reference group.