A Compendium of Common Heuristics, Misconceptions, and Biased Reasoning used in Statistical Thinking

Over the past decades, many researchers have identified ways of reasoning in the domain of statistics and probabilities that do not match statistics and probabilities results. Some of these inadequate conceptualizations are reviewed herein. They include, among others, the gambler’s fallacy, the law of small numbers, the misunderstanding of randomness, and they touch various aspects of statistics reasoning (sampling procedures, probability estimation, mean estimation, variance estimation, and inference). A classification is put forward.