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The clustering illusion is the tendency to erroneously consider the inevitable "streaks" or "clusters" arising in small samples from random distributions to be non-random. The illusion is caused by a human tendency to underpredict the amount of variability likely to appear in a small sample of random or pseudorandom data. [1]
Cognitive biases. In a chapter titled "Why Clever People Believe Stupid Things", Goldacre explains some of the appeal of alternative medicine ideas. Biases mentioned include confirmation bias, the availability heuristic, illusory superiority and the clustering illusion (the misperception of random data).
Misperception of patterns in random data is called pareidolia. Recent researches in neurosciences and cognitive sciences suggest to understand 'false pattern recognition', in the paradigm of predictive coding .
(ISBN 1-58799-190-X, New York : Random house, 2005) In 2005, a French version appeared, with many unique changes. [citation needed] The book has been translated into 20 languages, [5] and is reported to have sold over half a million copies. Further editions have been published by Penguin (softback, May 2007) and Random House (hardback, October ...
The tendency to perceive meaningful connections between unrelated things. [17] The following are types of apophenia: Clustering illusion, the tendency to overestimate the importance of small runs, streaks, or clusters in large samples of random data (that is, seeing phantom patterns).
For instance, in a study on Arabidopsis thaliana, biologically important regions of the plant's genome were found to be protected from mutations, and beneficial mutations were found to be more likely, i.e. mutation was "non-random in a way that benefits the plant". [556] [557] [558]
Here's how to distinguish "sundowning"—agitation or confusion later in the day in dementia patients—from typical aging, from doctors who treat older adults.
Students of statistics and probability theory sometimes develop misconceptions about the normal distribution, ideas that may seem plausible but are mathematically untrue. For example, it is sometimes mistakenly thought that two linearly uncorrelated, normally distributed random variables must be statistically independent.