It has never been easier to run complex analyses, visualize distributions, or test hypotheses. But statistical software can quickly become a “black box” when researchers do not fully understand what a p-value means, which assumptions matter, or when a test may be inappropriate.

This practical workshop is designed for researchers with little or no formal training in statistics who want to build a solid foundation for their scientific work. Participants combine conceptual understanding with hands-on Python coding using tools such as pandas and NumPy. Topics include sampling, descriptive statistics, probability distributions, hypothesis testing, Type I and II errors, p-values, confidence intervals, t-tests, and simple linear regression. A special “from scratch” approach helps demystify the calculations behind common tests. Participants will also practice interpreting results, checking assumptions, visualizing data, and applying the statistical workflow to their own research data.

Basic Inferential Statistics for Scientists