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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.
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.
For example, by truncating the bottom of a line or bar chart so that differences seem larger than they are. Or, by representing one-dimensional quantities on a pictogram by two- or three-dimensional objects to compare their sizes so that the reader forgets that the images do not scale the same way the quantities do.
In effort to meet this goal, researchers have turned to ideas from the machine learning field. This is a natural source of ideas, since the machine learning task can be described as turning background knowledge and examples (input) into knowledge (output). If data mining results in discovering meaningful patterns, data turns into information.
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]
Technology plays a role in both the spread and prevention of misinformation, with algorithms and artificial intelligence being employed to identify and combat false narratives. Media literacy: Promoting media literacy can empower individuals to critically evaluate information and discern between true and false statements.
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
The ratio of false positives (identifying an innocent traveler as a terrorist) to true positives (detecting a would-be terrorist) is, therefore, very high; and because almost every alarm is a false positive, the positive predictive value of these screening tests is very low.