pystatpower.proportion.single.inequality ¶

FUNCTION	DESCRIPTION
`solve_power`	Calculate the statistical power.
`solve_size`	Estimate the required sample size.
`solve_proportion`	Estimate the required proportion under the alternative hypothesis.
`solve_null_proportion`	Estimate the required proportion under the null hypothesis.

solve_power ¶

solve_power(
    *,
    proportion: float,
    null_proportion: float,
    size: int,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    method: Literal["exact", "z-p0", "z-phat"] = "exact",
    continuity_correction: bool = False,
) -> float

Calculate the statistical power.

PARAMETER	DESCRIPTION
`proportion`	Proportion under the alternative hypothesis. TYPE: `float`
`null_proportion`	Proportion under the null hypothesis. TYPE: `float`
`size`	Sample size. TYPE: `int`
`alternative`	Type of the alternative hypothesis: If `alternative` is `'two-sided'`, the alternative hypothesis is \(p ≠ p_0\) If `alternative` is `'greater'`, the alternative hypothesis is \(p > p_0\) If `alternative` is `'less'`, the alternative hypothesis is \(p < p_0\) TYPE: `Literal['two-sided', 'greater', 'less']` DEFAULT: `'two-sided'`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'greater'` or `'less'`, `alpha` represents the one-sided significance level. TYPE: `float` DEFAULT: `0.05`
`method`	The method used to construct the test statistic. `'exact'`: Binomial distribution. `'z-p0'`: Standard normal distribution (large sample approximation), using p0 to calculate the variance. `'z-phat'`: Standard normal distribution (large sample approximation), using phat to calculate the variance. TYPE: `Literal['exact', 'z-p0', 'z-phat']` DEFAULT: `'exact'`
`continuity_correction`	Whether to apply the continuity correction. TYPE: `bool` DEFAULT: `False`

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

solve_size ¶

solve_size(
    *,
    proportion: float,
    null_proportion: float,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    power: float = 0.8,
    method: Literal["exact", "z-p0", "z-phat"] = "exact",
    continuity_correction: bool = False,
) -> int

Estimate the required sample size.

PARAMETER	DESCRIPTION
`proportion`	Proportion under the alternative hypothesis. TYPE: `float`
`null_proportion`	Proportion under the null hypothesis. TYPE: `float`
`alternative`	Type of the alternative hypothesis: If `alternative` is `'two-sided'`, the alternative hypothesis is \(p ≠ p_0\) If `alternative` is `'greater'`, the alternative hypothesis is \(p > p_0\) If `alternative` is `'less'`, the alternative hypothesis is \(p < p_0\) TYPE: `Literal['two-sided', 'greater', 'less']` DEFAULT: `'two-sided'`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'greater'` or `'less'`, `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. `'exact'`: Binomial distribution. `'z-p0'`: Standard normal distribution (large sample approximation), using p0 to calculate the variance. `'z-phat'`: Standard normal distribution (large sample approximation), using phat to calculate the variance. TYPE: `Literal['exact', 'z-p0', 'z-phat']` DEFAULT: `'exact'`
`continuity_correction`	Whether to apply the continuity correction. TYPE: `bool` DEFAULT: `False`

RETURNS	DESCRIPTION
`int`	The required sample size.

solve_proportion ¶

solve_proportion(
    *,
    null_proportion: float,
    size: int,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    power: float = 0.8,
    method: Literal["exact", "z-p0", "z-phat"] = "exact",
    continuity_correction: bool = False,
    direction: Literal["greater", "less"] | None = None,
) -> float

Estimate the required proportion under the alternative hypothesis.

PARAMETER	DESCRIPTION
`null_proportion`	Proportion under the null hypothesis. TYPE: `float`
`size`	Sample size. TYPE: `int`
`alternative`	Type of the alternative hypothesis: If `alternative` is `'two-sided'`, the alternative hypothesis is \(p ≠ p_0\) If `alternative` is `'greater'`, the alternative hypothesis is \(p > p_0\) If `alternative` is `'less'`, the alternative hypothesis is \(p < p_0\) TYPE: `Literal['two-sided', 'greater', 'less']` DEFAULT: `'two-sided'`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'greater'` or `'less'`, `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. `'exact'`: Binomial distribution. `'z-p0'`: Standard normal distribution (large sample approximation), using p0 to calculate the variance. `'z-phat'`: Standard normal distribution (large sample approximation), using phat to calculate the variance. TYPE: `Literal['exact', 'z-p0', 'z-phat']` DEFAULT: `'exact'`
`continuity_correction`	Whether to apply the continuity correction. TYPE: `bool` DEFAULT: `False`
`direction`	The direction for the proportion under the alternative hypothesis relative to the proportion under the null hypothesis. `'greater'`: Search for the alternative proportion greater than the null proportion. `'less'`: Search for the alternative proportion less than the null proportion. Note If `alternative` is `'two-sided'`, this parameter is required. If `alternative` is `'greater'`, the search direction is automatically inferred to be `'greater'`, and this parameter is ignored. If `alternative` is `'less'`, the search direction is automatically inferred to be `'less'`, and this parameter is ignored. TYPE: `Literal['greater', 'less'] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`float`	The required proportion under the alternative hypothesis.

RAISES	DESCRIPTION
`ValueError`	If `alternative` is `'two-sided'` and `direction` is omitted.

solve_null_proportion ¶

solve_null_proportion(
    *,
    proportion: float,
    size: int,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    power: float = 0.8,
    method: Literal["z-p0", "z-phat"] = "z-phat",
    continuity_correction: bool = False,
    direction: Literal["greater", "less"] | None = None,
) -> float

Estimate the required proportion under the null hypothesis.

PARAMETER	DESCRIPTION
`proportion`	Proportion under the alternative hypothesis. TYPE: `float`
`size`	Sample size. TYPE: `int`
`alternative`	Type of the alternative hypothesis: If `alternative` is `'two-sided'`, the alternative hypothesis is \(p ≠ p_0\) If `alternative` is `'greater'`, the alternative hypothesis is \(p > p_0\) If `alternative` is `'less'`, the alternative hypothesis is \(p < p_0\) TYPE: `Literal['two-sided', 'greater', 'less']` DEFAULT: `'two-sided'`
`alpha`	Significance level. If `alternative` is `'two-sided'`, `alpha` represents the two-sided significance level. If `alternative` is `'greater'` or `'less'`, `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-p0'`: Standard normal distribution (large sample approximation), using p0 to calculate the variance. `'z-phat'`: Standard normal distribution (large sample approximation), using phat to calculate the variance. TYPE: `Literal['z-p0', 'z-phat']` DEFAULT: `'z-phat'`
`continuity_correction`	Whether to apply the continuity correction. TYPE: `bool` DEFAULT: `False`
`direction`	The direction for the proportion under the null hypothesis relative to the proportion under the alternative hypothesis. `'greater'`: Search for the null proportion greater than the alternative proportion. `'less'`: Search for the null proportion less than the alternative proportion. Note If `alternative` is `'two-sided'`, this parameter is required. If `alternative` is `'greater'`, the search direction is automatically inferred to be `'less'`, and this parameter is ignored. If `alternative` is `'less'`, the search direction is automatically inferred to be `'greater'`, and this parameter is ignored. TYPE: `Literal['greater', 'less'] \| None` DEFAULT: `None`

RETURNS	DESCRIPTION
`float`	The required proportion under the null hypothesis.

RAISES	DESCRIPTION
`ValueError`	If `alternative` is `'two-sided'` and `direction` is omitted.

Info

This solver does not support the exact test method, as minor fluctuations in null_proportion can cause oscillations in power, making it impossible to obtain an accurate null_proportion value under the given parameters.