TestResult

class TestResult(*args)

Test result data structure.

Warning

Tests results are not intended to be created manually. They are returned by the statistical tests implemented in the stattests subpackage. Constructor is therefore intentionally not documented.

Notes

The p-value of a test can be seen as the probability of observing a sample having a worst or equal statistic than the one that has been calculated on the tested sample, under the null hypothesis. This is the metric that is used for concluding the test with respect to the given accepted risk of committing a Type I error, that is an incorrect rejection of a true null hypothesis.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> dist, test_result = ot.FittingTest.Lilliefors(sample, ot.NormalFactory(), 0.01)
>>> print(test_result.getPValue())
0.4956...
>>> print(test_result.getThreshold())
0.01...
>>> print(test_result.getBinaryQualityMeasure())
True

Methods

getBinaryQualityMeasure()

Accessor to the test's binary conclusion.

getClassName()

Accessor to the object's name.

getId()

Accessor to the object's id.

getName()

Accessor to the object's name.

getPValue()

Accessor to the test's p-value.

getShadowedId()

Accessor to the object's shadowed id.

getStatistic()

Accessor to the used statistic for decision.

getThreshold()

Accessor to the accepted risk of committing a Type I error.

getVisibility()

Accessor to the object's visibility state.

hasName()

Test if the object is named.

hasVisibleName()

Test if the object has a distinguishable name.

setName(name)

Accessor to the object's name.

setShadowedId(id)

Accessor to the object's shadowed id.

setVisibility(visible)

Accessor to the object's visibility state.

getDescription

getTestType

setDescription
__init__(*args)
getBinaryQualityMeasure()

Accessor to the test’s binary conclusion.

Returns:
binary_measurebool, optional

Test conclusion: False if it can reject the null hypothesis, True if it cannot.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getPValue()

Accessor to the test’s p-value.

Returns:
p_valuefloat, 0 \leq p \leq 1

The test’s p-value.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:
idint

Internal unique identifier.

getStatistic()

Accessor to the used statistic for decision.

Returns:
statisticfloat

Measure used for the statistical test.

getThreshold()

Accessor to the accepted risk of committing a Type I error.

Returns:
thresholdfloat, 0 \leq \alpha \leq 1

Accepted risk of committing a Type I error.

getVisibility()

Accessor to the object’s visibility state.

Returns:
visiblebool

Visibility flag.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:
hasVisibleNamebool

True if the name is not empty and not the default one.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:
idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visiblebool

Visibility flag.

Examples using the class

Build and validate a linear model

Build and validate a linear model

Test a discrete distribution

Test a discrete distribution

Use the Kolmogorov/Lilliefors test

Use the Kolmogorov/Lilliefors test

Test identical distributions

Test identical distributions

Select fitted distributions

Select fitted distributions

Test Normality

Test Normality

Test independence

Test independence

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the p-value