Hypothesis Testing with Python
Hypothesis testing is a fundamental part of research because it allows you to draw conclusions about populations based on sample data. In research, you often want to know whether an observed effect or difference between groups is statistically significant, or if it could have occurred by chance. Hypothesis testing provides a structured way to answer these questions using probability and statistical reasoning.
12345678910111213141516171819import pandas as pd from scipy import stats # Sample research data: test scores for two groups data = { "group": ["A"] * 10 + ["B"] * 10, "score": [88, 90, 85, 87, 91, 86, 89, 88, 90, 92, 83, 80, 85, 82, 79, 84, 81, 83, 80, 82] } df = pd.DataFrame(data) # Split data into two groups group_a = df[df["group"] == "A"]["score"] group_b = df[df["group"] == "B"]["score"] # Perform independent t-test t_stat, p_value = stats.ttest_ind(group_a, group_b) print("t-statistic:", t_stat) print("p-value:", p_value)
When you perform a hypothesis test, the most important value to interpret is the p-value. The p-value tells you the probability of observing your data, or something more extreme, if the null hypothesis is true. In research, a small p-value (commonly less than 0.05) suggests that the observed difference is unlikely to have occurred by chance, and you may reject the null hypothesis. A larger p-value means there is not enough evidence to conclude a significant difference between groups, so you fail to reject the null hypothesis.
12345678# Extracting and reporting t-statistic and p-value result = stats.ttest_ind(group_a, group_b) t_statistic = result.statistic p_val = result.pvalue print(f"Independent t-test results:") print(f"t-statistic: {t_statistic:.3f}") print(f"p-value: {p_val:.4f}")
1. What does a p-value represent in hypothesis testing?
2. Which scipy function is used for an independent t-test?
3. What is the null hypothesis in a t-test?
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Hypothesis testing is a fundamental part of research because it allows you to draw conclusions about populations based on sample data. In research, you often want to know whether an observed effect or difference between groups is statistically significant, or if it could have occurred by chance. Hypothesis testing provides a structured way to answer these questions using probability and statistical reasoning.
12345678910111213141516171819import pandas as pd from scipy import stats # Sample research data: test scores for two groups data = { "group": ["A"] * 10 + ["B"] * 10, "score": [88, 90, 85, 87, 91, 86, 89, 88, 90, 92, 83, 80, 85, 82, 79, 84, 81, 83, 80, 82] } df = pd.DataFrame(data) # Split data into two groups group_a = df[df["group"] == "A"]["score"] group_b = df[df["group"] == "B"]["score"] # Perform independent t-test t_stat, p_value = stats.ttest_ind(group_a, group_b) print("t-statistic:", t_stat) print("p-value:", p_value)
When you perform a hypothesis test, the most important value to interpret is the p-value. The p-value tells you the probability of observing your data, or something more extreme, if the null hypothesis is true. In research, a small p-value (commonly less than 0.05) suggests that the observed difference is unlikely to have occurred by chance, and you may reject the null hypothesis. A larger p-value means there is not enough evidence to conclude a significant difference between groups, so you fail to reject the null hypothesis.
12345678# Extracting and reporting t-statistic and p-value result = stats.ttest_ind(group_a, group_b) t_statistic = result.statistic p_val = result.pvalue print(f"Independent t-test results:") print(f"t-statistic: {t_statistic:.3f}") print(f"p-value: {p_val:.4f}")
1. What does a p-value represent in hypothesis testing?
2. Which scipy function is used for an independent t-test?
3. What is the null hypothesis in a t-test?
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