left-out variable bias
[/ˈlɛft aʊt ˈvɛriəbəl ˈbaɪəs/]
nounpl: left-out variable biases
viés de variável omitida
1. A statistical bias that occurs in regression analysis when one or more relevant independent variables are excluded from the model, causing the estimated coefficients of the included variables to be biased and inconsistent
The study suffered from left-out variable bias because the researchers failed to include education level as a control variable in their wage regression model.
O estudo sofreu de viés de variável omitida porque os pesquisadores falharam em incluir o nível de educação como uma variável de controle no seu modelo de regressão de salários.
2. In econometrics, the problem that arises when important explanatory variables correlated with the included variables are not included in the regression equation
Economists must be careful to avoid left-out variable bias by including all theoretically relevant factors in their models.
Os economistas devem ter cuidado para evitar o viés de variável omitida incluindo todos os fatores teoricamente relevantes em seus modelos.
This is primarily a technical term used in academic and professional settings in both Brazil and Portugal, particularly in economics, statistics, and social sciences. The concept is fundamental to econometric methodology and is taught extensively in graduate-level statistics and economics courses throughout Portuguese-speaking countries and internationally.
Related Idioms & Phrases
missing the forest for the trees (when omitting key variables)
garbage in, garbage out (referring to flawed regression models)
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