Twenty tips for interpreting scientific claims

This list will help non-scientists to interrogate advisers and to grasp the limitations of evidence, say William J. Sutherland, David Spiegelhalter and Mark A. Burgman.

  • Differences and chance cause variation.
  • No measurement is exact.
  • Bias is rife
  • Bigger is usually better for sample size.
  • Correlation does not imply causation.
  • Regression to the mean can mislead.
  • Extrapolating beyond the data is risky.
  • Beware the base-rate fallacy.
  • Controls are important.
  • Randomization avoids bias.
  • Seek replication, not pseudoreplication.
  • Scientists are human.
  • Significance is significant.
  • Separate no effect from non-significance.
  • Effect size matters.
  • Study relevance limits generalizations.
  • Feelings influence risk perception.
  • Dependencies change the risks.
  • Data can be dredged or cherry picked.
  • Extreme measurements may mislead.

References
https://www.nature.com/news/policy-twenty-tips-for-interpreting-scientific-claims
Twenty tips for interpreting scientific claims [PDF]

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Hadi Nur

An ordinary man living in Johor Bahru, Malaysia who likes to write anything. This is table of content of my blog posts.