The fallacy of placing confidence in confidence intervals

Richard D. Morey, Rink Hoekstra, Jeffrey N. Rouder, Michael D. Lee, Eric-Jan Wagenmakers

OnderzoeksoutputAcademicpeer review

233 Citaten (Scopus)
662 Downloads (Pure)


Interval estimates – estimates of parameters that include an allowance for sampling uncertainty – have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; CIs are thought to be a guide to which parameter values are plausible or reasonable; and the confidence coefficient of the interval (e.g., 95 %) is thought to index the plausibility that the true parameter is included in the interval. We show in a number of examples that CIs do not necessarily have any of these properties, and can lead to unjustified or arbitrary inferences. For this reason, we caution against relying upon confidence interval theory to justify interval estimates, and suggest that other theories of interval estimation should be used instead.
Originele taal-2English
Pagina's (van-tot)103-123
Aantal pagina's21
TijdschriftPsychonomic Bulletin and Review
Nummer van het tijdschrift1
StatusPublished - 2016

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