pystatpower.correlation.inequality ¶

FUNCTION	DESCRIPTION
`solve_power`	Calculate the statistical power.
`solve_size`	Estimate the required sample size.
`solve_correlation`	Estimate the required correlation coefficient under the alternative hypothesis.
`solve_null_correlation`	Estimate the required correlation coefficient under the null hypothesis.

solve_power ¶

solve_power(
    *,
    null_correlation: float,
    correlation: float,
    size: int,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
) -> float

Calculate the statistical power.

PARAMETER	DESCRIPTION
`null_correlation`	Correlation coefficient under the null hypothesis. TYPE: `float`
`correlation`	Correlation coefficient 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 \(\rho \neq \rho_0\). If `alternative` is `'greater'`, the alternative hypothesis is \(\rho > \rho_0\). If `alternative` is `'less'`, the alternative hypothesis is \(\rho < \rho_0\). 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`

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

solve_size ¶

solve_size(
    *,
    null_correlation: float,
    correlation: float,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    power: float = 0.8,
) -> int

Estimate the required sample size.

PARAMETER	DESCRIPTION
`null_correlation`	Correlation coefficient under the null hypothesis. TYPE: `float`
`correlation`	Correlation coefficient under the alternative hypothesis. TYPE: `float`
`alternative`	Type of the alternative hypothesis. If `alternative` is `'two-sided'`, the alternative hypothesis is \(\rho \neq \rho_0\). If `alternative` is `'greater'`, the alternative hypothesis is \(\rho > \rho_0\). If `alternative` is `'less'`, the alternative hypothesis is \(\rho < \rho_0\). 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`

RETURNS	DESCRIPTION
`int`	The required sample size.

solve_correlation ¶

solve_correlation(
    *,
    null_correlation: float,
    size: int,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    power: float = 0.8,
    direction: Literal["greater", "less"] | None = None,
) -> float

Estimate the required correlation coefficient under the alternative hypothesis.

PARAMETER	DESCRIPTION
`null_correlation`	Correlation coefficient 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 \(\rho \neq \rho_0\). If `alternative` is `'greater'`, the alternative hypothesis is \(\rho > \rho_0\). If `alternative` is `'less'`, the alternative hypothesis is \(\rho < \rho_0\). 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`
`direction`	The direction for the correlation coefficient under the alternative hypothesis relative to the correlation coefficient under the null hypothesis. `'greater'`: Search for the alternative correlation coefficient greater than the null correlation coefficient. `'less'`: Search for the alternative correlation coefficient less than the null correlation coefficient. 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 correlation coefficient under the alternative hypothesis.

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

solve_null_correlation ¶

solve_null_correlation(
    *,
    correlation: float,
    size: int,
    alternative: Literal["two-sided", "greater", "less"] = "two-sided",
    alpha: float = 0.05,
    power: float = 0.8,
    direction: Literal["greater", "less"] | None = None,
) -> float

Estimate the required correlation coefficient under the null hypothesis.

PARAMETER	DESCRIPTION
`correlation`	Correlation coefficient 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 \(\rho \neq \rho_0\). If `alternative` is `'greater'`, the alternative hypothesis is \(\rho > \rho_0\). If `alternative` is `'less'`, the alternative hypothesis is \(\rho < \rho_0\). 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`
`direction`	The direction for the correlation coefficient under the null hypothesis relative to the correlation coefficient under the alternative hypothesis. `'greater'`: Search for the null correlation coefficient greater than the alternative correlation coefficient. `'less'`: Search for the null correlation coefficient less than the alternative correlation coefficient. 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 correlation coefficient under the null hypothesis.

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