“Fixed mindsets” might be why we don’t understand statistics

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In summary, the article discusses our difficulty in understanding statistics, using the example of a high-profile murder trial where flawed statistical reasoning led to a wrongful conviction. A new study suggests that our resistance to change and fixed mindsets may be to blame for our preference for complicated solutions over simpler, more intuitive ones. The conversation further highlights the common misunderstanding of statistical terms and the importance of considering variability in data analysis.
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https://arstechnica.com/science/2018/10/fixed-mindsets-might-be-why-we-dont-understand-statistics/

The article outlines our difficulty of understanding statistics in everyday problems or while on juries.

In 1999, an English solicitor named Sally Clark went on trial for the murder of her two infant sons. She claimed both succumbed to sudden infant death syndrome. An expert witness for the prosecution, Sir Roy Meadow, argued that the odds of SIDS claiming two children from such an affluent family were 1 in 73 million, likening it to the odds of backing an 80-1 horse in the Grand National four years in a row and winning every time. The jury convicted Clark to life in prison.

But the Royal Statistical Society issued a statement after the verdict insisting that Meadow had erred in his calculation and that there was "no statistical basis" for his stated figure. Clark's conviction was overturned on appeal in January 2003, and the case has become a canonical example of the consequences of flawed statistical reasoning.

A new study in Frontiers in Psychology examined why people struggle so much to solve statistical problems, particularly why we show a marked preference for complicated solutions over simpler, more intuitive ones. Chalk it up to our resistance to change. The study concluded that fixed mindsets are to blame: we tend to stick with the familiar methods we learned in school, blinding us to the existence of a simpler solution.
 
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Its okay, I'm a robot too.

We bots have to stick together, right?
 
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Tell me about it. Most people (and that includes a lot of academicians) understand the concept of "mean value" - full stop. What they do not understand is that:
  • The "mean value" is equivalent to the "center of gravity" in physics - nice to know, but only of restricted usefulness
  • In order to say something useful about a set of data, you must include the "standard deviation" (which is equivalent to the "moment of inertia" in physics).
I could go on for several pages, but I would only get frustrated thinking about it.

(For the record - I am not a statistician. I learned about the importance of these things in Physics 101 - Measurement Theory).
 
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Svein said:
Tell me about it. Most people (and that includes a lot of academicians) understand the concept of "mean value" - full stop. What they do not understand is that:
  • The "mean value" is equivalent to the "center of gravity" in physics - nice to know, but only of restricted usefulness
  • In order to say something useful about a set of data, you must include the "standard deviation" (which is equivalent to the "moment of inertia" in physics).
I could go on for several pages, but I would only get frustrated thinking about it.

(For the record - I am not a statistician. I learned about the importance of these things in Physics 101 - Measurement Theory).
There is a book detailing poor knowledge and understanding of Statistical terms and ideas :" The Flaw of Averages" details the over-reliance on the mean at the exoense of other measurements. If you go to the north pole for 1 year and a coat will last you 3 months on average, will you only take 4 coats with you, or will you consider the variability ( variance/sd)? Sure, to all here in PF this is likely trivial but apparently not so much so outside of PF.
 

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