pystatpower.models.proportion.single.inequality ¶

Functions:

Name	Description
`solve_power`	Calculate the power for a one-sample proportion test.
`solve_size`	Estimate the sample size required for a one-sample proportion test.
`solve_null_proportion`	Estimate the null proportion (p0) required to achieve a target power for a one-sample proportion test.
`solve_proportion`	Estimate the alternative proportion (p1) required to achieve a target power for a one-sample proportion test.

solve_power ¶

solve_power(*, null_proportion: float, proportion: float, size: int, alternative: Literal['one-sided', 'two-sided'] = 'two-sided', alpha: float = 0.05, phat: bool = False, continuity_correction: bool = False) -> float

Calculate the power for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`null_proportion`	`float`	The proportion specified under the null hypothesis (p0). Must be in the interval (0, 1).	required
`proportion`	`float`	The expected or observed proportion under the alternative hypothesis (p1). Must be in the interval (0, 1).	required
`size`	`int`	Total number of independent observations (sample size). Must be >= 1.	required
`alternative`	`Literal['one-sided', 'two-sided']`	Direction of the test: "two-sided": \(H1: p1 \neq p_0\) "one-sided": \(H1: p1 > p_0\) or \(p1 < p_0\) Default is "two-sided".	`'two-sided'`
`alpha`	`float`	Significance level (Type I error rate). Defaults is 0.05.	`0.05`
`phat`	`bool`	Whether to use sample proportion to calculate standard deviation. Defaults is False.	`False`
`continuity_correction`	`bool`	Whether to apply continuity correction to the normal approximation. Defaults is False.	`False`

Returns:

Name	Type	Description
`power`	`float`	The calculated power of the test, in the range [0, 1].

solve_size ¶

solve_size(*, null_proportion: float, proportion: float, alternative: Literal['one-sided', 'two-sided'] = 'two-sided', alpha: float = 0.05, power: float = 0.8, phat: bool = False, continuity_correction: bool = False) -> int

Estimate the sample size required for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`null_proportion`	`float`	The proportion specified under the null hypothesis (p0). Must be in the interval (0, 1).	required
`proportion`	`float`	The expected proportion under the alternative hypothesis (p1). Must be in the interval (0, 1).	required
`alternative`	`Literal['one-sided', 'two-sided']`	Direction of the test: "two-sided": \(H1: p \neq p_0\) "one-sided": \(H1: p > p_0\) or \(p < p_0\) Default is "two-sided".	`'two-sided'`
`alpha`	`float`	Significance level (Type I error rate). Defaults is 0.05.	`0.05`
`power`	`float`	Desired statistical power (1 - Type II error rate). Defaults is 0.80.	`0.8`
`phat`	`bool`	Whether to use sample proportion to calculate standard deviation. Defaults is False.	`False`
`continuity_correction`	`bool`	Whether to apply continuity correction to the normal approximation. Defaults is False.	`False`

Returns:

Name	Type	Description
`size`	`float`	The minimum sample size (rounded up to the nearest integer) required to achieve the target power.

solve_null_proportion ¶

solve_null_proportion(*, proportion: float, size: int, alternative: Literal['one-sided', 'two-sided'] = 'two-sided', alpha: float = 0.05, power: float = 0.8, phat: bool = False, continuity_correction: bool = False, proportion_selection: Literal['lower', 'upper'] = 'lower') -> float

Estimate the null proportion (p0) required to achieve a target power for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`proportion`	`float`	The expected proportion under the alternative hypothesis (p1). Must be in the interval (0, 1).	required
`size`	`int`	Total number of independent observations (sample size). Must be >= 1.	required
`alternative`	`Literal['one-sided', 'two-sided']`	Direction of the test: "two-sided": \(H1: p \neq p_0\) "one-sided": \(H1: p > p_0\) or \(p < p_0\) Default is "two-sided".	`'two-sided'`
`alpha`	`float`	Significance level (Type I error rate). Defaults is 0.05.	`0.05`
`power`	`float`	Desired statistical power (1 - Type II error rate). Defaults is 0.80.	`0.8`
`phat`	`bool`	Whether to use sample proportion to calculate standard deviation. Defaults is False.	`False`
`continuity_correction`	`bool`	Whether to apply continuity correction to the normal approximation. Defaults is False.	`False`
`proportion_selection`	`Literal['lower', 'upper']`	Selection strategy when two valid null proportions exist: "lower": Returns the solution where p0 < p1. "upper": Returns the solution where p0 > p1. If only one solution exists in (0, 1), this parameter is ignored. Defaults is "lower".	`'lower'`

Returns:

Name	Type	Description
`null_proportion`	`float`	The estimated null proportion (p0).

solve_proportion ¶

solve_proportion(*, null_proportion: float, size: int, alternative: Literal['one-sided', 'two-sided'] = 'two-sided', alpha: float = 0.05, power: float = 0.8, phat: bool = False, continuity_correction: bool = False, proportion_selection: Literal['lower', 'upper'] = 'upper')

Estimate the alternative proportion (p1) required to achieve a target power for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`null_proportion`	`float`	The proportion specified under the null hypothesis (p0). Must be in the interval (0, 1).	required
`size`	`int`	Total number of independent observations (sample size). Must be >= 1.	required
`alternative`	`Literal['one-sided', 'two-sided']`	Direction of the test: "two-sided": \(H1: p \neq p_0\) "one-sided": \(H1: p > p_0\) or \(p < p_0\) Default is "two-sided".	`'two-sided'`
`alpha`	`float`	Significance level (Type I error rate). Defaults is 0.05.	`0.05`
`power`	`float`	Desired statistical power (1 - Type II error rate). Defaults is 0.80.	`0.8`
`phat`	`bool`	Whether to use sample proportion to calculate standard deviation. Defaults is False.	`False`
`continuity_correction`	`bool`	Whether to apply continuity correction to the normal approximation. Defaults is False.	`False`
`proportion_selection`	`Literal['lower', 'upper']`	Selection strategy when two valid alternative proportions exist: "lower": Returns the solution where p1 < p0. "upper": Returns the solution where p1 > p0. If only one solution exists in (0, 1), this parameter is ignored. Defaults is "upper".	`'upper'`

Returns:

Name	Type	Description
`proportion`	`float`	The estimated alternative proportion (p1).