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In the 1960s and 1970s, it became a standard textbook introduction to the subject of statistics for many college students. It has become one of the best-selling statistics books in history, with over one and a half million copies sold in the English-language edition. [1] It has also been widely translated.
The source is a subject matter expert, not a statistics expert. [6] The source may incorrectly use a method or interpret a result. The source is a statistician, not a subject matter expert. [7] An expert should know when the numbers being compared describe different things.
In statistics, a misleading graph, also known as a distorted graph, is a graph that misrepresents data, constituting a misuse of statistics and with the result that an incorrect conclusion may be derived from it. Graphs may be misleading by being excessively complex or poorly constructed.
Darrell Huff (July 15, 1913 – June 27, 2001) was an American writer, and is best known as the author of How to Lie with Statistics (1954), the best-selling statistics book of the second half of the twentieth century. [1]
How Not to Be Wrong explains the mathematics behind some of simplest day-to-day thinking. [4] It then goes into more complex decisions people make. [5] [6] For example, Ellenberg explains many misconceptions about lotteries and whether or not they can be mathematically beaten.
The question is whether knowing the warden's answer changes the prisoner's chances of being pardoned. This problem is equivalent to the Monty Hall problem; the prisoner asking the question still has a 1 / 3 chance of being pardoned but his unnamed colleague has a 2 / 3 chance.
The origin of the phrase "Lies, damned lies, and statistics" is unclear, but Mark Twain attributed it to Benjamin Disraeli [1] "Lies, damned lies, and statistics" is a phrase describing the persuasive power of statistics to bolster weak arguments, "one of the best, and best-known" critiques of applied statistics. [2]
Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, [ 1 ] [ 2 ] [ 3 ] and is particularly problematic when frequency data are unduly given ...