Mistakes in Experimental Design and Interpretation

Mistakes in Experimental Design and Interpretation

Humans are very good at detecting patterns, but rather poor at detecting randomness. We expect random incidents of cancer to be spread homogeneously, when in fact true randomness results in random clusters, not homogeneity. It is a mistake for an experiment to consider a pool of 47,000 possibilities, and then only report on the 7 cases that seem interesting.

A proper experiment states its hypothesis before gathering evidence and then puts the hypothesis to the test. Remember when you did your seventh grade science fair experiment: you made up a hypothesis first (“Hamsters will get fatter from eating Lucky Charms than Wheaties”) and then did the experiment to confirm or refute the hypothesis. You can’t just make up a hypothesis after the fact to fit the data.

This is an excellent article discussing very common errors in how people use data. We have tendencies that lead us to draw faulty conclusions from data. Given that it is important to understand what common mistakes are made to help us counter the natural tendencies.

Related: Seeing Patterns Where None ExistsIllusions, Optical and OtherUnderstanding DataDangers of Forgetting the Proxy Nature of DataHow to Deal with False Research Findingsdescriptive “theory” and normative theory

2 thoughts on “Mistakes in Experimental Design and Interpretation

  1. Pingback: CuriousCat: Data Can't Lie

  2. Pingback: Curious Cat Management Blog ยป Statistical Learning as the Ultimate Agile Development Tool by Peter Norvig

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