W31 - Data Analysis Fundamentals — Statistics

Learning the fundamentals of data analysis — statistics. Recently I came across some useful material, such as how food-delivery teams scientifically design A/B tests and analyze the data. I’ve also been thinking about how, after our engineers report business metrics, we can statistically analyze those data to extract more valuable insights. I found that addressing these kinds of problems requires common foundational knowledge — statistics. In some simple scenarios, experience-based knowledge is still adequate. But once data complexity increases, or when you want to uncover deeper, more reliable information, available knowledge becomes insufficient. The most common question is how to demonstrate the relationship between an experiment sample’s characteristics and the population’s characteristics when we can’t access the full population data. I’ve followed a data-analysis community newsletter, “BA Toolbox Newsletter.” Overall, it focuses on analytical thinking for practical problems and is very enlightening for nonexperts like us. However, when it comes to analytical details — concepts such as confidence intervals and hypothesis testing — gaps in knowledge create major barriers to understanding. These aren’t especially advanced topics, so I’ve recently reviewed undergraduate-level statistics and plan to write it up to benefit more people.