Generalization Bias in Science

Published on August 31, 2022

In the lively world of science, researchers often make assumptions based on limited data and apply those assumptions to larger populations. This cognitive process, known as scientific induction, has long been thought to be a conscious inference where scientists carefully evaluate the applicability of their findings. However, a new account challenges this assumption and suggests that scientists may be subject to a generalization bias. Just like a busy bee collecting nectar from various flowers, researchers may unintentionally generalize their results without sufficient evidence, leading to overgeneralized conclusions. This generalization bias in scientific induction has far-reaching implications and has even been linked to the replication crisis in the sciences. To address this issue, it is crucial to supplement proposed interventions with cognitive debiasing strategies against generalization bias. By tackling this bias head-on, we can improve the reliability and accuracy of scientific research.

Abstract
Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science.

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