get_critical_value#

pycafee.sample.studentdistribution.StudentDistribution.get_critical_value(self, gl, alfa=None, which=None)#

This function returns the critical value of the two-side or one-side Student’s t distribution. This is just a wrapper around stats.t.ppf [1].

Parameters
glint, higher than 1

The degree of freedom of the sample

alfafloat

The level of significance, 0.0 < alfa < 1.0.

whichstr, optional

The parameter which controls the t distribution to be ploted. The options are:

  • None or "two-side" (default): two-side

  • "one-side": one-side

Returns
resulttuple

The critical values for a par gl and alpha, where

  • The first element is the higher critical value;

  • The second element is the lower critical value;

  • The third element is the corresponding alpha value;

  • The fourth element is the distribution used from the which parameter;

See also

draw

Notes

The critical values for the sample are obtained using the scipy percent point function [1]:

stats.t.ppf(1-alfa/2, gl) or stats.t.ppf(alfa/2, gl) # for the two-side distribution
stats.t.ppf(1-alfa, gl) or stats.t.ppf(alfa, gl) # for the one-side distribution

References

1(1,2)

SCIPY. scipy.stats.t. Available at: www.scipy.org. Access on: 10 May. 2022.

Examples

Getting the critical values for 4 degrees of freedom at 95% of confidence level (two-side)

>>> from pycafee.sample.studentdistribution import StudentDistribution
>>> student = StudentDistribution()
>>> result = student.get_critical_value(4)
>>> print(result)
Student(Upper=2.7764451051977996, Lower=-2.7764451051977996, Alpha=0.05, Distribution='two-side')

Getting the critical values for 5 degrees of freedom at 90% of confidence level (two-side)

>>> from pycafee.sample.studentdistribution import StudentDistribution
>>> student = StudentDistribution()
>>> result = student.get_critical_value(5, alfa=0.1)
>>> print(result)
Student(Upper=2.0150483726691575, Lower=-2.0150483726691575, Alpha=0.1, Distribution='two-side')

Getting the critical values for 4 degrees of freedom at 95% of confidence level (one-side)

>>> from pycafee.sample.studentdistribution import StudentDistribution
>>> student = StudentDistribution()
>>> result = student.get_critical_value(4, which="one-side")
Student(Upper=2.13184678133629, Lower=-2.13184678133629, Alpha=0.05, Distribution='one-side')

Getting the critical values for 5 degrees of freedom at 90% of confidence level (one-side)

>>> from pycafee.sample.studentdistribution import StudentDistribution
>>> student = StudentDistribution()
>>> result = student.get_critical_value(5, which="one-side", alfa=0.1)
>>> print(result)
Student(Upper=1.4758840487820273, Lower=-1.475884048782027, Alpha=0.1, Distribution='one-side')

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