📊 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗼𝘄 𝗪𝗲 𝗟𝗲𝗮𝗿𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 We all learned statistical distributions in college, but the theory-first approach made them feel like a memorization exercise rather than a practical tool. The number of times people learn and forget them is probably an interesting distribution itself! Here's what I've found works better: Start with the metric type based on things you encounter in your data analysis work, then map to the right distribution. So, instead of memorizing a seemingly endless list of arbitrarty distributions, you focus on the metrics you actually encounter in your work. This practical-first framing makes statistical distributions more manageable and immediately useful for applied work. Check out the infographic below for a quick weekend refresher! 👇 #DataScience #Statistics #Analytics #MachineLearning
Very useful. There are practical A/B testing related implications of understanding the metric/distribution relationships. The choice of metric and its underlying distribution can substantially affect statistical power. By selecting or transforming a metric that reduces variance, you can reach significance faster (often a case/goal/desire with AB testing).
Best way to memorize distributions or other statistical principles for sure. Thanks for the concise summary in an e-commerce context, Arslan, wholeheartedly agree.