W31 - Data Analysis Fundamentals — Statistics

Learn the fundamentals of data analysis — statistics. Lately I’ve come across some useful materials, for example how food-delivery teams scientifically design A/B tests and perform data analysis. I’ve also considered how, after our engineering team reports business metrics, we can statistically analyze the data to surface more valuable insights. Addressing these kinds of problems requires a common body of knowledge — statistics. In simple scenarios, experience-based intuition often suffices. But once data complexity increases, or when you want to extract deeper, more reliable information, the available knowledge quickly becomes insufficient. The most common challenge is how to demonstrate the relationship between sample characteristics and population characteristics when we cannot access the full population data. I’ve followed a data-analysis community newsletter, “BA Toolbox Newsletter.” Overall it focuses on problem-driven analytical thinking and has been enlightening for non-experts like us. However, when it delves into analytical details — for example confidence intervals or hypothesis testing — gaps in my knowledge create significant barriers to understanding. These aren’t especially advanced topics, so I recently reviewed undergraduate-level statistics and am preparing to write it up to benefit more people.