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A two sample t-test in Python is a statistical test used to determine if two independent samples have the same population mean. It is used to compare two sets of data and can be performed with the SciPy library using the ttest_ind() function, which takes two arrays of sample data as inputs. The output of the t-test is a calculated t-statistic and a two-tailed p-value, which can be used to determine if the samples have the same mean.
A two sample t-test is used to test whether or not the means of two populations are equal.
This tutorial explains how to conduct a two sample t-test in Python.
Example: Two Sample t-Test in Python
Researchers want to know whether or not two different species of plants have the same mean height. To test this, they collect a simple random sample of 20 plants from each species.
Use the following steps to conduct a two sample t-test to determine if the two species of plants have the same height.
Step 1: Create the data.
First, we’ll create two arrays to hold the measurements of each group of 20 plants:
import numpy as np group1 = np.array([14, 15, 15, 16, 13, 8, 14, 17, 16, 14, 19, 20, 21, 15, 15, 16, 16, 13, 14, 12]) group2 = np.array([15, 17, 14, 17, 14, 8, 12, 19, 19, 14, 17, 22, 24, 16, 13, 16, 13, 18, 15, 13])
Step 2: Conduct a two sample t-test.
Next, we’ll use the ttest_ind() function from the scipy.stats library to conduct a two sample t-test, which uses the following syntax:
ttest_ind(a, b, equal_var=True)
where:
- a: an array of sample observations for group 1
- b: an array of sample observations for group 2
- equal_var: if True, perform a standard independent 2 sample t-test that assumes equal population variances. If False, perform Welch’s t-test, which does not assume equal population variances. This is True by default.
Before we perform the test, we need to decide if we’ll assume the two populations have equal variances or not. As a rule of thumb, we can assume the populations have equal variances if the ratio of the larger sample variance to the smaller sample variance is less than 4:1.
#find variance for each group
print(np.var(group1), np.var(group2))
7.73 12.26
The ratio of the larger sample variance to the smaller sample variance is 12.26 / 7.73 = 1.586, which is less than 4. This means we can assume that the population variances are equal.
Thus, we can proceed to perform the two sample t-test with equal variances:
import scipy.stats as stats #perform two sample t-test with equal variances stats.ttest_ind(a=group1, b=group2, equal_var=True) (statistic=-0.6337, pvalue=0.53005)
The t test statistic is -0.6337 and the corresponding two-sided p-value is 0.53005.
Step 3: Interpret the results.
The two hypotheses for this particular two sample t-test are as follows:
H0: µ1 = µ2 (the two population means are equal)
HA: µ1 ≠µ2 (the two population means are not equal)
Because the p-value of our test (0.53005) is greater than alpha = 0.05, we fail to reject the null hypothesis of the test. We do not have sufficient evidence to say that the mean height of plants between the two populations is different.
How to Conduct a One Sample T-Test in Python
How to Conduct a Paired Samples T-Test in Python