HypoResult

class hypotestx.core.result.HypoResult(test_name: str, statistic: float, p_value: float, effect_size: float | None = None, effect_size_name: str | None = None, confidence_interval: tuple | None = None, degrees_of_freedom: int | tuple | None = None, sample_sizes: int | tuple | None = None, assumptions_met: Dict[str, bool] | None = None, interpretation: str | None = None, data_summary: Dict[str, Any] | None = None, alpha: float = 0.05, alternative: str = 'two-sided', **kwargs)

Bases: object

Standardized result object for hypothesis tests

property is_significant: bool

Check if result is statistically significant

property effect_magnitude: str

Interpret effect size magnitude using Cohen’s conventions for the appropriate measure.

summary(verbose: bool = False) → str

Human-readable summary of results

to_dict() → Dict[str, Any]

Convert result to dictionary

plot(kind: str = 'auto')

Visualise this result.

Delegates to hypotestx.explore.visualize.plot_result().

Parameters:

kind (str) – "auto" (default), "p_value", "bar", "box".

Return type:

matplotlib.figure.Figure

Raises:

ImportError – If matplotlib is not installed. Install with pip install matplotlib or pip install hypotestx[visualization].

Fields Reference

Field	Type	Description
test_name	str	Human-readable test name
statistic	float	Test statistic value (t, F, χ², U, W, r, …)
p_value	float	p-value
effect_size	float | None	Effect size (Cohen’s d, r, η², Cramér’s V, …)
effect_size_name	str | None	Name of the effect size measure
confidence_interval	tuple[float, float] | None	(lower, upper) confidence interval
degrees_of_freedom	int | float | None	Degrees of freedom
sample_sizes	int | tuple | None	Per-group or total sample size(s)
assumptions_met	dict[str, bool]	Assumption check results
interpretation	str | None	Plain-English interpretation
alpha	float	Significance level used
alternative	str	"two-sided", "greater", or "less"
routing_confidence	float	Routing confidence: 1.0 (LLM) or 0.6 (fallback)
routing_source	str	"llm" or "fallback"
is_significant	bool (property)	True if p_value < alpha
effect_magnitude	str (property)	"negligible", "small", "medium", or "large"