biostats.one_sample_t_test

biostats.one_sample_t_test(data, variable, expect, kind='two-side')

Test whether the mean value of a variable is different from the expected value.

Parameters:

datapandas.DataFrame

The input data. Must contain at least one numeric column.

variablestr

The numeric variable that we want to calculate the mean value of.

expectfloat or int

The expected value.

kindstr

• “two-side” : Test whether the mean value is different from the expected value.

• “greater” : Test whether the mean value is greater than the expected value.

• “less” : Test whether the mean value is less than the expected value.

Returns:

summarypandas.DataFrame

The estimation, standard error, and confidence interval of the mean value.

resultpandas.DataFrame

The degree of freedom, t statistic, and p-value of the test.

See also

two_sample_t_test

Compare the mean values between two groups.

paired_t_test

Compare the mean values between two paired groups.

median_test

The non-parametric version of one-sample t-test.

Examples

>>> import biostats as bs
>>> data = bs.dataset("one_sample_t_test.csv")
>>> data
   Angle
0  120.6
1  116.4
2  117.2
3  118.1
4  114.1
5  116.9
6  113.3
7  121.1
8  116.9
9  117.0

We want to test whether the mean value of Angle is different from 120.

>>> summary, result = bs.one_sample_t_test(data=data, variable="Angle", expect=120, kind="two-side")
>>> summary
       Estimate  Std. Error  95% CI: Lower  95% CI: Upper
Angle    117.16    0.769155      115.42005      118.89995

The mean value of Angle and its 95% confidence interval are given.

>>> result
       D.F.  t Statistic   p-value    
Model     9    -3.692362  0.004979  **

The p-value < 0.01, so the mean value of Angle is significantly different from the expected value.