The Fallacy of Placing Confidence in Confidence Intervals
Welcome to the web site for the upcoming paper "The Fallacy of Placing Confidence in Confidence Intervals." Here you will find a number of resources connected to the paper, including the itself, the supplement, teaching resources and in the future, links to discussion of the content.
The paper is accepted for publication in Psychonomic Bulletin & Review.
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
The Fallacy of Placing Confidence in Confidence Intervals
I just read through this, and it sounds like they're trying to squish a frequentist interpretation on a Bayesian tool. They keep saying how the confidence intervals don't correspond with reality, but confidence intervals are supposed to be measuring degrees of belief. Am I missing something here?
If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
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