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date: 02 June 2020

Abstract and Keywords

Sampling explanations focus on biases in the information available to managers instead of biases in how managers process the available information. Managers seldom have access to a random or representative sample of outcomes. The available sample of outcomes is often subject to a selection bias. This chapter reviews recent research on three sampling biases with important implications for organizations: undersampling of failure, the hot-stove effect, and underweighting rare events in small samples. There is evidence from experiments as well as field studies that these sample biases significantly impact decision making. In particular, whether managers will choose a risky or a safe alternative depends on the type of bias in data available to them.

Keywords: sampling, decision making, organizational learning, judgment biases, selection bias, hot-stove effect, risk taking, exploration

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