Traduzione e analisi delle parole tramite l'intelligenza artificiale ChatGPT
In questa pagina puoi ottenere un'analisi dettagliata di una parola o frase, prodotta utilizzando la migliore tecnologia di intelligenza artificiale fino ad oggi:
- come viene usata la parola
- frequenza di utilizzo
- è usato più spesso nel discorso orale o scritto
- opzioni di traduzione delle parole
- esempi di utilizzo (varie frasi con traduzione)
- etimologia
Cosa (chi) è negative outcome - definizione
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.jpg?width=200 "Negative storey numbers in an elevator.")
![futures]] prices on the [[New York Mercantile Exchange]] in March, April, and May 2020](https://commons.wikimedia.org/wiki/Special:FilePath/Selected North American crude oil prices March 1 through May 13, 2020 (49941454556).png?width=200 "futures]] prices on the [[New York Mercantile Exchange]] in March, April, and May 2020")
![[[Gram-positive]] and -negative [[bacteria]] are differentiated chiefly by their [[cell wall]] structure](https://commons.wikimedia.org/wiki/Special:FilePath/Gram-Cell-wall.svg?width=200 "[[Gram-positive]] and -negative [[bacteria]] are differentiated chiefly by their [[cell wall]] structure")
![Gram-negative [[cell wall]] structure](https://commons.wikimedia.org/wiki/Special:FilePath/Gram negative cell wall.svg?width=200 "Gram-negative [[cell wall]] structure")
Wikipedia
Outcome Bias, also known as the Disparity Fallacy and Equity Fallacy, is a form of confirmation bias, and by extension a cognitive bias. This error arises when a decision is based on the outcome of previous events, without regard to how the past events developed. Outcome bias does not involve analysis of factors that lead to a previous event, and instead de-emphasizes the events preceding the outcomes and overemphasizes the outcome. Unlike hindsight bias, outcome bias does not involve the distortion of past events.
While similar to the hindsight bias, the two phenomena are markedly different. Hindsight bias focuses on memory distortion to favor the actor, while the outcome bias focuses exclusively on weighting the past outcome heavier than other pieces of information in deciding if a past decision was correct.
