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In computer science, garbage in, garbage out (GIGO) is the concept that flawed, biased or poor quality ("garbage") information or input produces a result or output of similar ("garbage") quality. The adage points to the need to improve data quality in, for example, programming. Rubbish in, rubbish out (RIRO) is an alternate wording. [1] [2] [3]
The phrase Follow the money was mentioned by Henry E. Peterson at the 1974 Senate Judiciary Committee hearings as Earl J. Silbert was nominated to U.S. Attorney. [3] A 1975 book by Clive Borrell and Brian Cashinella, Crime in Britain Today, also uses the phrase.
Several news organizations cited the same quote but without the apostrophe amid criticism that Biden had appeared to direct his garbage comment at Trump supporters more broadly. Biden also sought ...
The lesson here: If you haven't actually read a work, don't purport to quote from it, or your assumptions about what it meant (if you even get the out-of-context words right at all) are apt to be embarrassingly incorrect. As the old joke goes: "Never assume, for when you do, you make an ass out of u and me."
The yellow metal can’t be printed at will by central banks like fiat money, and its safe-haven status means demand typically increases during times of uncertainty. And it’s clear what Kiyosaki ...
It became, in the words of The New Palgrave Dictionary of Economics, "the standard analysis of quality control inspection". The dictionary adds, "Like many of Friedman's contributions, in retrospect it seems remarkably simple and obvious to apply basic economic ideas to quality control; that, however, is a measure of his genius."
“Politico just added an apostrophe to the quote, making it seem like Biden is just labeling one supporter as garbage. This is literal state propaganda,” popular X account End Wokeness fumed .
George Box. The phrase "all models are wrong" was first attributed to George Box in a 1976 paper published in the Journal of the American Statistical Association.In the paper, Box uses the phrase to refer to the limitations of models, arguing that while no model is ever completely accurate, simpler models can still provide valuable insights if applied judiciously. [1]