Data Interpretation: Avoiding Common Pitfalls
Effective data interpretation requires context, sufficient sample sizes, and acknowledgment of confounding factors.
What is this page about?
A guide to interpreting data correctly, the reasoning layer that determines whether a finding is real and actionable rather than an artifact, distinct from analysis and visualization, opening with John Snow's 1854 Broad Street cholera investigation. It covers putting context before calculation, the space between correlation and causation and the three mechanisms of spurious correlation, statistical versus practical significance and the multiple-comparison problem, mean versus median, handling missing data, the dangers of extrapolation, and a red-team approach to interpretation.
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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Challenger launched at ~36F (coldest Shuttle launch); 29F was not the launch temperature.
Beforeproposed launch temperature of 29 degrees Fahrenheit
Afterlaunch-time temperature of about 36 degrees Fahrenheit
Why: Verified live content already correctly states the Challenger launch-time temperature as about 36 degrees Fahrenheit. FAQ and excerpt checked, no leftover fabrication found. No action needed.
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1 contributor has checked "Data Interpretation: Avoiding Common Pitfalls" 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.