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Learning Statistics with Python

Performing a t-test in PythonPerforming a t-test in Python

To conduct a t-test in Python, all you have to do is specify the alternative hypothesis and indicate whether variances are roughly equal (homogeneous).

The ttest_ind() function within scipy.stats handles the rest. Below is the syntax:

Parameters:

  • a — the first sample.
  • b — the second sample.
  • equal_var — set to True if variances are approximately equal, and False if they are not.
  • alternative — the type of alternative hypothesis:
    • 'two-sided' — indicates that the means are not equal.
    • 'less' — implies that the first mean is less than the second.
    • 'greater' — implies that the first mean is greater than the second.

Return values:

  • statistic — the value of the t statistic.
  • pvalue — the p-value.

We are interested in the pvalue. If it is lower than α(usually 0.05), then the t statistic is in the critical region, so we should accept the alternative hypothesis. And if pvalue is greater than α — we accept the null hypothesis that means are equal.

Here is an example of applying the t-test to our heights dataset:

Everything was clear?

Section 6. Chapter 6