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One usable definition is: "Misuse of Statistics: Using numbers in such a manner that – either by intent or through ignorance or carelessness – the conclusions are unjustified or incorrect." [1] The "numbers" include misleading graphics discussed in other sources. The term is not commonly encountered in statistics texts and there is no ...
False precision (also called overprecision, fake precision, misplaced precision, and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias.
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.
Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Unsourced material may be challenged and removed. Find sources: "How to Lie with Statistics" – news · newspapers · books · scholar · JSTOR ( February 2022 ) ( Learn how and when to remove this message )
How to Read Numbers: A Guide to Statistics in the News (and Knowing When to Trust Them) is a 2021 British book by Tom and David Chivers. It describes misleading uses of statistics in the news, with contemporary examples about the COVID-19 pandemic, healthcare, politics and crime. The book was conceived by the authors, who are cousins, in early ...
The total number of people living in extreme absolute poverty globally, by the widely used metric of $1.00/day (in 1990 U.S. dollars) has decreased over the last several decades, but most people surveyed in several countries incorrectly think it has increased or stayed the same. [205] However, this depends on the poverty line calculation used.
A group of 58 researchers is calling for a new, better way to measure obesity.. The global team’s recommendations were published in The Lancet Diabetes & Endocrinology on Jan. 14.
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.