The bias bias

Henry Brighton, Gerd Gigerenzer

Research output: Contribution to journalArticleScientificpeer-review

72 Citations (Scopus)


In marketing and finance, surprisingly simple models sometimes predict more accurately than more complex, sophisticated models. Here, we address the question of when and why simple models succeed - or fail - by framing the forecasting problem in terms of the bias-variance dilemma. Controllable error in forecasting consists of two components, the "bias" and the "variance". We argue that the benefits of simplicity are often overlooked because of a pervasive "bias bias": the importance of the bias component of prediction error is inflated, and the variance component of prediction error, which reflects an oversensitivity of a model to different samples from the same population, is neglected. Using the study of cognitive heuristics, we discuss how to reduce variance by ignoring weights, attributes, and dependencies between attributes, and thus make better decisions. Bias and variance, we argue, offer a more insightful perspective on the benefits of simplicity than Occam''s razor.
Original languageEnglish
Pages (from-to)1772-1784
Number of pages13
JournalJournal of Business Research
Publication statusPublished - 1 Aug 2015
Externally publishedYes


  • Bias bias
  • Bias-variance dilemma
  • Occam's razor
  • Out-of-sample prediction
  • Simple heuristics
  • Uncertainty


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