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In addition to accuracy of individual statements, it is an objective that articles provide overall accurate coverage of the topic, albeit the latter is less clear-cut. This includes balanced coverage, with inclusion weighted by degree of significance, informativeness on the topic, and directness of relevance to the topic. The article should not ...
A study by Yale University cognitive scientists Gordon Pennycook and David G. Rand found that Facebook tags of fake articles "did significantly reduce their perceived accuracy relative to a control without tags, but only modestly". [18] A Dartmouth study led by Brendan Nyhan found that Facebook tags had a greater impact than the Yale study found.
Wikipedia articles can have poor quality in many ways including self-contradictions. [2] Those poor articles require improvement. Large platforms including YouTube [3] and Facebook [4] use Wikipedia's content to confirm the accuracy of the information in their own media collections.
Articles should be based on reliable, independent, published sources with a reputation for fact-checking and accuracy. This means that we publish only the analysis, views, and opinions of reliable authors, and not those of Wikipedians, who have read and interpreted primary source material for themselves.
The article reasonably covers the topic, and does not contain obvious omissions or inaccuracies. It contains a large proportion of the material necessary for an A-Class article, although some sections may need expansion, and some less important topics may be missing. The article has a defined structure.
In principle, all articles should be based on reliable, independent, published sources with a reputation for fact-checking and accuracy. When writing about a topic, basing content on the best respected and most authoritative reliable sources helps to prevent bias, undue weight, and other NPOV disagreements.
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Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10] As such, it compares estimates of pre- and post-test probability.