Common Analytics Mistakes and Their Implications
Explore common analytics errors that can skew results, including confusing correlation with causation.
What is this page about?
An explainer of the most common and damaging data-analytics mistakes, illustrated with real cases where correct calculations were applied to the wrong question. It covers correlation-versus-causation and confounding variables (with the Google Flu Trends and HP retention examples), small-sample unreliability, survivorship bias, cherry-picking and the Texas Sharpshooter fallacy, p-hacking and the replication crisis, Goodhart's Law, Simpson's Paradox, base-rate neglect, and the ecological fallacy, then outlines systematic defenses against these errors.
What has been corrected on this page?
Every accepted correction to this page is recorded with the exact change, so readers can see how the page improved over time.
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3 corrections applied: Sears warring-divisions reporting was Mina Kimes (2013 Bloomberg Businessweek), not the unverified Lynn Cowan / Shira Ovide 2021 NYT attribution. | Ranehill et al. power-posing replication was published in 2015, not 2017. | Lead author of the 2018 growth-mindset meta-analysis was Victoria F. Sisk, not 'Alexander Sisk' (Alexander Burgoyne was a co-author).
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Why: All 3 named corrections already correctly applied in body text: Mina Kimes/2013 Bloomberg Businessweek for the Sears reporting, Eva Ranehill's 2015 power-posing replication study, and Victoria F. Sisk (not Alexander Sisk) as lead author of the 2018 growth-mindset meta-analysis. FAQ checked, no fabrication present. No changes needed.
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1 contributor has checked "Common Analytics Mistakes and Their Implications" on When Notes Fly. Each name below links to that person's public CitePep profile, where every contribution they have made is listed with the exact change they proposed.