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Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.
Recently, a lot of work has gone into helping detect and identify fake news through machine learning and artificial intelligence. [76] [77] [78] In 2018, researchers at MIT's CSAIL created and tested a machine learning algorithm to identify false information by looking for common patterns, words, and symbols that typically appear in fake news. [79]
Spreading false information can also seriously impede the effective and efficient use of the information available on social media. [124] An emerging trend in the online information environment is "a shift away from public discourse to private, more ephemeral, messaging ", which is a challenge to counter misinformation.
For example, if a pharmaceutical company wishes to explore the effect of a medication on the common cold but the data sample only includes men, any conclusions made from that data will be biased towards how the medication affects men rather than people in general. That means the information would be incomplete and not useful for deciding if the ...
For example, log scales may give a height of 1 for a value of 10 in the data and a height of 6 for a value of 1,000,000 (10 6) in the data. Log scales and variants are commonly used, for instance, for the volcanic explosivity index, the Richter scale for earthquakes, the magnitude of stars, and the pH of acidic and alkaline solutions.
The findings offer an indication of just how quickly the technology has been embraced by people seeking to spread false information. But researchers warned that AI is still just one way in which ...
Malinformation is a controversial term for information which is based on fact, but removed from its original context in order to mislead, harm, or manipulate. [1] The term was first coined by Hossein Derakhshan and was used in a co-authored report titled "Information Disorder: Toward an interdisciplinary framework for research and policy making". [2]
Post hoc alteration of data inclusion based on arbitrary or subjective reasons, including: Cherry picking , which actually is not selection bias, but confirmation bias , when specific subsets of data are chosen to support a conclusion (e.g. citing examples of plane crashes as evidence of airline flight being unsafe, while ignoring the far more ...