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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [2]Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.
Key elements were the establishment of Associated Press in the 1850s (short factual material needed), Ralph Pulitzer of the New York World (his Bureau of Accuracy and Fair Play, 1912), Henry Luce and Time magazine (original working title: Facts), and the famous fact-checking department of The New Yorker. More recently, the mainstream media has ...
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.
The intentional dissemination of misstatements (disinformation) is commonly termed as deception or lying, while unintentional inaccuracies may arise from misconceptions, misinformation, or mistakes. Although the word fallacy is sometimes used as a synonym for false statement , that is not how the word is used in most formal contexts.
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.
Arophobia; Acephobia; Adultism; Anti-albinism; Anti-autism; Anti-homelessness; Anti-drug addicts; Anti-intellectualism; Anti-intersex; Anti-left handedness; Anti-Masonry
"the usefulness, accuracy, and correctness of data for its application" [10] Arguably, in all these cases, "data quality" is a comparison of the actual state of a particular set of data to a desired state, with the desired state being typically referred to as "fit for use," "to specification," "meeting consumer expectations," "free of defect ...