t-test in Python
To perform a t-test in Python, you typically use the scipy library, specifically the scipy.stats.ttest_ind function for comparing the means of two independent samples. This function takes two arrays or lists of numeric values representing your groups and returns two important values: the t-statistic and the p-value. These outputs help you determine whether the difference in means between your groups is statistically significant, based on the hypotheses you have set up. The process of running a t-test in Python is straightforward and can be integrated into your analysis workflow with just a few lines of code.
123456789101112import numpy as np from scipy import stats # Simulated data for two independent groups group_a = np.array([23, 21, 19, 24, 20, 22, 25, 23, 21, 20]) group_b = np.array([30, 29, 31, 28, 32, 30, 29, 31, 28, 30]) # Perform an independent two-sample t-test t_statistic, p_value = stats.ttest_ind(group_a, group_b) print("t-statistic:", t_statistic) print("p-value:", p_value)
When you run ttest_ind, you receive two key outputs:
- t-statistic: measures how different the group means are, relative to the variation in your data. A larger absolute t-statistic means a bigger difference between the groups;
- p-value: tells you the probability of observing such a difference (or a more extreme one) if the null hypothesis were true—meaning, if there were actually no difference between the group means.
A small p-value (commonly less than 0.05) indicates that the observed difference is unlikely to be due to chance. In this case, you would reject the null hypothesis.
A large p-value suggests that any difference could plausibly be due to random variation, so you would not reject the null hypothesis.
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Can you explain what the null hypothesis is in this context?
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To perform a t-test in Python, you typically use the scipy library, specifically the scipy.stats.ttest_ind function for comparing the means of two independent samples. This function takes two arrays or lists of numeric values representing your groups and returns two important values: the t-statistic and the p-value. These outputs help you determine whether the difference in means between your groups is statistically significant, based on the hypotheses you have set up. The process of running a t-test in Python is straightforward and can be integrated into your analysis workflow with just a few lines of code.
123456789101112import numpy as np from scipy import stats # Simulated data for two independent groups group_a = np.array([23, 21, 19, 24, 20, 22, 25, 23, 21, 20]) group_b = np.array([30, 29, 31, 28, 32, 30, 29, 31, 28, 30]) # Perform an independent two-sample t-test t_statistic, p_value = stats.ttest_ind(group_a, group_b) print("t-statistic:", t_statistic) print("p-value:", p_value)
When you run ttest_ind, you receive two key outputs:
- t-statistic: measures how different the group means are, relative to the variation in your data. A larger absolute t-statistic means a bigger difference between the groups;
- p-value: tells you the probability of observing such a difference (or a more extreme one) if the null hypothesis were true—meaning, if there were actually no difference between the group means.
A small p-value (commonly less than 0.05) indicates that the observed difference is unlikely to be due to chance. In this case, you would reject the null hypothesis.
A large p-value suggests that any difference could plausibly be due to random variation, so you would not reject the null hypothesis.
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