Performing T-test statistics in Python

Using Python for t-test Statistics is a mini book. It is a friendly, hands‑on guide for students who want to learn how to compare means using real code — no prior Python experience required. Inside you’ll find clear explanations of one‑sample, independent (Student’s and Welch’s) and paired t‑tests, step‑by‑step workflows in Google Colab, and practical guidance on checking assumptions, reporting results, and interpreting effect sizes and confidence intervals.

Learn to load and wrangle data with pandas, run tests with SciPy and the user‑friendly Pingouin package, visualise results with seaborn, and work with downloadable open datasets so you can practice right away. Each chapter contains runnable Colab snippets, diagnostic tips, and concise reporting templates so you can produce reproducible analyses for homework, projects, or small research studies.

Perfect for beginners in statistics or Python, this book turns abstract concepts into hands‑on experience: run the code, read the output, and understand what the numbers actually mean.