Whether judging the worth of someone else’s statistical work or your own, it’s always helpful to keep in mind the “MAGIC” criteria spelled out by Robert Abelson in Statistics As Principled Argument. Here are the five:
"1. Magnitude: How big is the effect?" Statistically significant or not, is the size of the effect too trivial to matter? "2. Articulation: How precisely stated is it?" Is the finding too vague or muddled to be useful? "3. Generality: How widely does it apply?" Does it only matter for one city, one college major, one health condition? "4. Interest: How interesting is it?" Will it get anyone’s attention? "5. Credibility: How believable is it?" Not that counter-intuitive findings should be ignored. But they should be especially questioned: extraordinary claims require extraordinary evidence. These five criteria are worth keeping close at hand to help you decide when a statistical finding is really actionable. Contact: [email protected]
2 Comments
John Wilson
8/28/2024 11:48:48 am
Nice mnemonic! And I like the point about questioning but not simply rejecting suspicious-looking data. The stuff from engineering about discarding outlying data for only legit reasons definitely fits here too.
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Ken
8/28/2024 08:55:05 pm
Good tips! I see too many articles failing to account for #1.
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AuthorRoland B. Stark Archives
August 2024
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