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In others, it is purposeful and for the gain of the perpetrator. Often, when the statistics are false or misapplied, this constitutes a statistical fallacy. The consequences of such misinterpretations can be quite severe. For example, in medical science, correcting a falsehood may take decades and cost lives.
McNamara's interest in quantitative figures is also seen in Project 100,000 aka McNamara's Folly: by lowering admission standards to the military, enlistment was increased. Key to this decision was the idea that one soldier is, in the abstract, more or less equal to another, and that with the right training and superior equipment, he would ...
Logical Fallacies, Literacy Education Online; Informal Fallacies, Texas State University page on informal fallacies; Stephen's Guide to the Logical Fallacies (mirror) Visualization: Rhetological Fallacies, Information is Beautiful; Master List of Logical Fallacies, University of Texas at El Paso; Fallacies, Internet Encyclopedia of Philosophy
Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions.
Neglect of probability, the tendency to completely disregard probability when making a decision under uncertainty. [52] Scope neglect or scope insensitivity, the tendency to be insensitive to the size of a problem when evaluating it. For example, being willing to pay as much to save 2,000 children or 20,000 children.
[14] [15] It would have been incorrect, and an example of prosecutor's fallacy, to rely solely on the "1 in 400" figure to deduce that a given person matching the sample would be likely to be the culprit. Frequency tree of 100 000 battered American women showing the base rate fallacy made by the defense in the O. J. Simpson murder trial
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. [1]
Extension neglect [a] is a type of cognitive bias which occurs when the sample size is ignored when its determination is relevant. [1] For instance, when reading an article about a scientific study, extension neglect occurs when the reader ignores the number of people involved in the study (sample size) but still makes inferences about a population based on the sample.