The central limit theorem and why it might ring your statistical bells

“With some exceptions, any average of a large number of similar terms will have a normal, bell-shaped distribution.” This powerful statistical discovery by Pierre Laplace in 1810 as described by Sharon Bertsch McGrayne, The Theory That Would Not Die (Yale Univ. 2011) at 6, means that if a law department takes the average of successive invoices, for example, those averages will have a Gaussian distribution. In accordance with the central limit theory, if every month you take the average of new cases that month and the previous month, the series will distribute normally.

So what? You can use standard deviations and many other statistical tools when the collection of metrics takes the form of the well-understood bell curve (See my post of March 12, 2009: bell curves with 8 references.).

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