Hypothesis Testing with Men's and Women's Soccer Matches

This project was a great hands‑on way to apply hypothesis testing to a real‑world sports dataset. I especially appreciated how it guided me through the full statistical workflow—from cleaning and preparing FIFA World Cup match data to selecting the correct test based on assumptions like independence, sample size, and normality. The step‑by‑step structure made it easy to connect theory with practice. Visualizing the distributions, checking normality, and comparing parametric vs. non‑parametric options helped reinforce why test selection matters. Running the one‑sided hypothesis test and interpreting the p‑value at a 10% significance level felt intuitive and meaningful because the context (goals scored in men’s vs. women’s matches) was engaging. Overall, this project strengthened my confidence in choosing and executing hypothesis tests in Python. It’s a solid learning experience for anyone who wants to move beyond formulas and understand how statistical decisions are made in real analysi