Experimentation terms: Understanding power, uncertainty, and detectable effects
Blog post from Mixpanel
Mixpanel's guide on product experimentation emphasizes the importance of understanding statistical foundations without needing extensive statistical expertise. It explains the differences between Frequentist and Bayesian approaches to experimentation, highlighting the former's traditional method of fixed sample sizes and p-values, and the latter's focus on probabilities and flexibility. The guide covers key concepts such as statistical power, sample size, minimum detectable effect (MDE), and confidence intervals, stressing their roles in ensuring reliable and meaningful experimental results. It also addresses sequential testing and the risks of "peeking" at results prematurely, advocating for proper planning and the use of modern experimentation platforms to maintain statistical validity. Ultimately, the guide underscores the need for a structured approach to experimentation, where understanding and applying these statistical concepts can lead to trustworthy and impactful product decisions.
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