Practical Statistical Analysis with Pingouin and Python

Traditional statistical tools in Python can often feel overly complex and require writing verbose, hard-to-maintain code. If you need a straightforward, modern way to run statistical tests and analyze data, the Pingouin library is the perfect solution. This text-based course guides you from the fundamental concepts of statistics to performing advanced tests with just a few lines of clean Python code. You will understand how to select the right test for your data, run the analysis, and interpret the results with confidence. In this course, you will: 1. Understand core statistical concepts including hypothesis testing, p-values, and effect sizes. 2. Perform parametric and non-parametric tests such as t-tests, Mann-Whitney U, and Wilcoxon. 3. Execute analysis of variance (ANOVA) and post-hoc tests with simple, readable code. 4. Calculate correlation matrices and build linear regression models using modern Python workflows. 5. Integrate Pingouin seamlessly with Pandas DataFrames for clean data manipulation. 6. Interpret statistical outputs and export summary tables for reporting. You will start by exploring foundational statistical theory and the core structure of the Pingouin library. From there, you will progress through practical, text-based examples that demonstrate how to run and interpret diverse statistical tests on real-world datasets. This course is designed for beginners to statistics and data analysis, with no prior experience in Pingouin required. A basic familiarity with Python and Pandas is helpful but not mandatory. Start mastering clean, efficient statistical analysis in Python today.