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.
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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 Exists – Illusions, Optical and Other – Understanding Data – Dangers of Forgetting the Proxy Nature of Data – How to Deal with False Research Findings – descriptive “theory” and normative theory
August 9th, 2007 at 10:08 am
Many people don’t understand the difference between being manipulated because they can’t understand what the data really says and data itself “lying” (which, of course, doesn’t even make sense). The same confusion can come in when someone just draws the wrong conclusion from the data that exists (and them blames the data for “lying” instead of themselves for drawing a faulty conclusion).
November 5th, 2009 at 9:13 am
He looks at the advantages of machine learning versus hand programming every case (for example spelling correction)…