A two-sample T-test is used to compare the means of two independent groups to determine if they are significantly different from each other.

import numpy as np
import scipy.stats as stats

data_group1 = np.array([14, 15, 15, 16, 13, 8, 14, 17, 16, 14, 19, 20, 21, 15, 15, 16, 16, 13, 14, 12])
data_group2 = np.array([15, 17, 14, 17, 14, 8, 12, 19, 19, 14, 17, 22, 24, 16, 13, 16, 13, 18, 15, 13])

t_statistic, p_value = stats.ttest_ind(a=data_group1, b=data_group2, equal_var=True)stats.ttest_ind(a=data_group1, b=data_group2, equal_var=True)
print(f"T-statistic: {t_statistic}")
print(f"P-value: {p_value}")

The two-sample T-test results are as follows:

**t-statistic**: -0.6337397070250238
**p-value**: 0.5300471010405257

Given the p-value of 0.5300, we fail to reject the null hypothesis. This means that there is no statistically significant difference between the means of the two groups, and any observed difference is likely due to random variation rather than a true underlying difference. A p-value of 0.5300 means there is a 53% chance that the difference between the groups we observed is just due to randomness.