Statistical Hypothesis Testing, From Assumptions to Verdict

I need to test whether {{describe the difference or relationship you're investigating}}.

Context:
- Dataset: {{describe the two groups or conditions being compared}}
- Sample sizes: Group A = `{{n}}`, Group B = `{{n}}`
- Variable being compared: {{variable name and type}}
- Significance level: {{0.05 unless you have a reason for something else}}

Perform:
1. State the null and alternative hypotheses in plain language
2. Check assumptions for the appropriate test (normality, equal variance, independence)
3. Select and justify the correct statistical test (t-test, Mann-Whitney, chi-square, ANOVA, etc.)
4. Run the test and report: test statistic, p-value, and confidence interval
5. Calculate effect size (Cohen's d, odds ratio, or appropriate measure)
6. Interpret the result in plain language: "There is/isn't sufficient evidence that..."
7. State the practical significance (is the difference big enough to matter, even if statistically significant?)

Write the Python/R code to run this analysis from data loading to conclusion.