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In the 2009 book Dirty rotten strategies by Ian I. Mitroff and Abraham Silvers described type III and type IV errors providing many examples of both developing good answers to the wrong questions (III) and deliberately selecting the wrong questions for intensive and skilled investigation (IV). Most of the examples have nothing to do with ...
In fact, the larger the sample size, the smaller the minimum effect needed to produce a statistically significant p-value (see effect size). Issues 1 and 2 can be illustrated by analogy to the Prosecutor's Fallacy in their shared underlying 2×2 contingency table format, where the user's convenient 90° rotation of attention replaces the ...
This statistics -related article is a stub. You can help Wikipedia by expanding it.
A seemingly simple question about the number of words in the English language immediately encounters questions about archaic forms, accounting for prefixes and suffixes, multiple definitions of a word, variant spellings, dialects, fanciful creations (like ectoplastistics from ectoplasm and statistics), [10] technical vocabulary, and so on.
There are many longstanding unsolved problems in mathematics for which a solution has still not yet been found. The notable unsolved problems in statistics are generally of a different flavor; according to John Tukey, [1] "difficulties in identifying problems have delayed statistics far more than difficulties in solving problems."
Confusion of the inverse, also called the conditional probability fallacy or the inverse fallacy, is a logical fallacy whereupon a conditional probability is equated with its inverse; that is, given two events A and B, the probability of A happening given that B has happened is assumed to be about the same as the probability of B given A, when there is actually no evidence for this assumption.
The answer to the first question is 2 / 3 , as is shown correctly by the "simple" solutions. But the answer to the second question is now different: the conditional probability the car is behind door 1 or door 2 given the host has opened door 3 (the door on the right) is 1 / 2 .
The law of truly large numbers (a statistical adage), attributed to Persi Diaconis and Frederick Mosteller, states that with a large enough number of independent samples, any highly implausible (i.e. unlikely in any single sample, but with constant probability strictly greater than 0 in any sample) result is likely to be observed. [1]
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