pystatpower.models.proportion.single.inequality ¶

Functions:

Name	Description
`solve_power`	Calculate the statistical power for a one-sample proportion test.
`solve_size`	Estimate the required sample size for a one-sample proportion test.
`solve_null_proportion`	Estimate the required proportion under the null hypothesis (\(p_0\)) for a one-sample proportion test.
`solve_proportion`	Estimate the required proportion under the alternative hypothesis (\(p_1\)) 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 statistical power for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`null_proportion`	`float`	The proportion specified under the null hypothesis (\(p_0\)). Must be in the interval (0, 1).	required
`proportion`	`float`	The expected or observed proportion under the alternative hypothesis (\(p_1\)). 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']`	Type iof the alternative hypothesis: `'two-sided'`: \(H_1: p_1 \neq p_0\) `'one-sided'`: \(H_1: p_1 > p_0\) or \(p_1 < p_0\)	`'two-sided'`
`alpha`	`float`	Significance level.	`0.05`
`phat`	`bool`	Whether or not to use sample proportion to calculate standard deviation.	`False`
`continuity_correction`	`bool`	Whether or not to apply Yate's continuity correction.	`False`

Returns:

Type	Description
`float`	The calculated power of the test.

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 required sample size for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`null_proportion`	`float`	The proportion specified under the null hypothesis (\(p_0\)). Must be in the interval (0, 1).	required
`proportion`	`float`	The expected proportion under the alternative hypothesis (\(p_1\)). Must be in the interval (0, 1).	required
`alternative`	`Literal['one-sided', 'two-sided']`	Type of the alternative hypothesis: `'two-sided'`: \(H_1: p \neq p_0\) `'one-sided'`: \(H_1: p > p_0\) or \(p < p_0\)	`'two-sided'`
`alpha`	`float`	Significance level.	`0.05`
`power`	`float`	Desired statistical power (1 - Type II error rate).	`0.8`
`phat`	`bool`	Whether or not to use sample proportion to calculate standard deviation.	`False`
`continuity_correction`	`bool`	Whether or not to apply Yate's continuity correction.	`False`

Returns:

Type	Description
`int`	The required sample size.

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,
    search_direction: Literal["below", "above"] = "below",
) -> float

Estimate the required proportion under the null hypothesis (\(p_0\)) for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`proportion`	`float`	The expected proportion under the alternative hypothesis (\(p_1\)). 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']`	Type of the alternative hypothesis: `'two-sided'`: \(H_1: p \neq p_0\) `'one-sided'`: \(H_1: p > p_0\) or \(p < p_0\)	`'two-sided'`
`alpha`	`float`	Significance level.	`0.05`
`power`	`float`	Desired statistical power (1 - Type II error rate).	`0.8`
`phat`	`bool`	Whether or not to use sample proportion to calculate standard deviation.	`False`
`continuity_correction`	`bool`	Whether or not to apply Yate's continuity correction.	`False`
`search_direction`	`Literal['below', 'above']`	Selection strategy when two valid null proportions exist: "below": Returns the solution where \(p_0 < p_1\). "above": Returns the solution where \(p_0 > p_1\). If only one solution exists in (0, 1), this parameter is ignored.	`'below'`

Returns:

Type	Description
`float`	The estimated null proportion (\(p_0\)).

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,
    search_direction: Literal["below", "above"] = "above",
) -> float

Estimate the required proportion under the alternative hypothesis (\(p_1\)) for a one-sample proportion test.

Parameters:

Name	Type	Description	Default
`null_proportion`	`float`	The proportion specified under the null hypothesis (\(p_0\)). 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']`	Type of the alternative hypothesis: `'two-sided'`: \(H_1: p \neq p_0\) `'one-sided'`: \(H_1: p > p_0\) or \(p < p_0\)	`'two-sided'`
`alpha`	`float`	Significance level.	`0.05`
`power`	`float`	Desired statistical power (1 - Type II error rate).	`0.8`
`phat`	`bool`	Whether or not to use sample proportion to calculate standard deviation.	`False`
`continuity_correction`	`bool`	Whether or not to apply Yate's continuity correction.	`False`
`search_direction`	`Literal['below', 'above']`	Selection strategy when two valid alternative proportions exist: "below": Returns the solution where \(p_1 < p_0\). "above": Returns the solution where \(p_1 > p_0\). If only one solution exists in (0, 1), this parameter is ignored.	`'above'`

Returns:

Type	Description
`float`	The estimated alternative proportion (\(p_1\)).