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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.
As AI has made fake content much easier to produce, a growing number of American teenagers say they are being misled by AI-generated photos, videos or other content on the internet, a new study shows.
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.
The purpose of the requirements to recognize and report hypothetical conditions is to limit the potential for the communication of the appraisal to imply that the hypothetical condition may be plausible or probable and limit the potential for the a user of assignment results being misled by the appraisal regarding its actual value or regarding ...
The book is a brief, breezy illustrated volume outlining the misuse of statistics and errors in the interpretation of statistics, and how errors create incorrect conclusions. In the 1960s and 1970s, it became a standard textbook introduction to the subject of statistics for many college students.
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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.