Build a Practical Hypothesis Testing Example in Statistics for Data Science

Define the build target

For Hypothesis Testing, test whether two independent sample means differ, report the null hypothesis, test statistic, p-value, significance decision, confidence interval, and a short interpretation in context. Build the boundary case using this implementation lens: Use measured variables, distributions, probability models, samples, uncertainty intervals, null hypotheses, p-values, regression diagnostics, assumptions, and decision context.

Keep the Hypothesis Testing build centered on these technical constraints: The null hypothesis describes the reference or no-effect claim being tested. The alternative hypothesis describes the direction or difference supported when the data are inconsistent with the null model. Apply them through this path lens: Use measured variables, distributions, probability models, samples, uncertainty intervals, null hypotheses, p-values, regression diagnostics, assumptions, and decision context. Use measured variables, distributions, probability models, samples, uncertainty intervals, null hypotheses, p-values, regression diagnostics, assumptions, and decision context.

Implement the core behavior

Implement Hypothesis Testing around the module artifact—a hypothesis-test report with assumptions, p-value, effect estimate, and contextual conclusion—and keep the implementation specific to this path context: Use measured variables, distributions, probability models, samples, uncertainty intervals, null hypotheses, p-values, regression diagnostics, assumptions, and decision context.

from scipy import stats

control = [18, 20, 19, 21, 20, 22, 18, 19]
variant = [22, 24, 21, 25, 23, 24, 22, 26]
alpha = 0.05
statistic, p_value = stats.ttest_ind(control, variant, equal_var=False)
print(f't={statistic:.3f}, p-value={p_value:.4f}')
print('Reject H0' if p_value < alpha else 'Do not reject H0')

python3 hypothesis_test.py

A t statistic, p-value, and decision relative to the chosen significance level.

practice/\n├── README.md\n├── hypothesis-testing-build.py\n└── evidence/\n    └── expected-result.txt

Run the complete path

Run one realistic Hypothesis Testing case end to end and record the required evidence: the test definition, statistic, p-value, confidence interval, and decision rule. Interpret the result through this path context: Use measured variables, distributions, probability models, samples, uncertainty intervals, null hypotheses, p-values, regression diagnostics, assumptions, and decision context.

Change one meaningful condition

Modify one condition central to Hypothesis Testing using this path context: Use measured variables, distributions, probability models, samples, uncertainty intervals, null hypotheses, p-values, regression diagnostics, assumptions, and decision context. Predict the new result before rerunning the same workflow.

Verify the artifact

Your deliverable is a hypothesis-test report with assumptions, p-value, effect estimate, and contextual conclusion.

Common mistakes to avoid

• If assumptions are questionable, inspect the data and consider a robust or nonparametric alternative.
• Do not switch between one-sided and two-sided hypotheses after seeing the data.
• Do not interpret p > 0.05 as proof that the groups are identical.
• Check multiple-testing risk when many hypotheses are tested at once.