The distribution of a random process will usually be symmetrically distributed about the mean. Sometimes early results may be skewed. In theory results should approach the expected distribution over time as a more complete sampling is taken. This underlies the need to have an adequate sample size, to have appropriate controls and not to draw conclusion based on early results.

Significance: An intervention aimed at a group or characteristic that is very different from the average may initially appear to be more or less successful than it really is (Morton and Torgerson).



(1) number of results available

(2) randomness and bias of the study

(3) stability in the underlying system

(4) within-subject variability or measurement error


You would expect a regression to the mean if:

(1) The initial results involve a small sample.

(2) Subjects and results are random and unbiased.

(3) The system is the same as when the distribution was determined.

(4) There is large within-subject variability or measurement error (Linden et al).


You would expect results to continue to be skewed when the study is complete if:

(1) Almost all of the results have been collected.

(2) Data is biased or nonrandom.

(3) Something fundamental has changed.

(4) There is a small within-subject variability or measurement error.


The magnitude of the regression to the mean effect can be calculated using statistical formulae.

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