*[Excerpts taken from the article “Are confidence intervals better termed ‘uncertainty intervals’?” by Andrew Gelman and Sander Greenland, published in the *__BMJ__.]

__BMJ__.]

**Are confidence intervals better termed “uncertainty intervals?”**

**Yes—Andrew Gelman**

###### “Confidence intervals can be a useful summary in model based inference. But the term should be “uncertainty interval,” not “confidence interval”…”

###### “Officially, all that can be interpreted are the long term average properties of the procedure that’s used to construct the interval, but people tend to interpret each interval implicitly in a bayesian way—that is, by acting as though there’s a 95% probability that any given interval contains the true value.”

###### “Using confidence intervals to rule out zero (or other parameter values) involves all of the well known problems of significance testing. So, rather than constructing this convoluted thing called a confidence procedure, which is defined to have certain properties on average but can’t generally be interpreted for individual cases, I prefer to aim for an uncertainty interval, using the most appropriate statistical methods to get there.”

###### “Let’s use the term “uncertainty interval” instead of “confidence interval.” The uncertainty interval tells us how much uncertainty we have.”

**No—Sander Greenland**

###### “The label “95% confidence interval” evokes the idea that we should invest the interval with 95/5 (19:1) betting odds that the observed interval contains the true value…”

###### “…the 95% is overconfident because it takes no account of procedural problems and model uncertainties that should reduce confidence in statistical results. Those possibilities include uncontrolled confounding, selection bias, measurement error, unaccounted-for model selection, and outright data corruption.”

###### “…no conventional interval adequately accounts for procedural problems that afflict data generation or for uncertainties about the statistical assumptions.”

###### “Nonetheless, all values in a conventional 95% interval can be described as highly compatible with data under the background statistical assumptions, in the very narrow sense of having P>0.05 under those assumptions.”

###### “In equivalent terms: given any value in the interval and the background assumptions, the data should not seem very surprising. This leads to the intentionally modest term “compatibility interval” as a replacement for ‘confidence interval.'”

###### “In summary, both “confidence interval” and “uncertainty interval” are deceptive terms, for they insinuate that we have achieved valid quantification of confidence or uncertainty despite omitting important uncertainty sources.

###### “Replacing “significance” and “confidence” labels with “compatibility” is a simple step to encourage honest reporting of how little we can confidently conclude from our data.”

###### To read the full article, **click here**. (NOTE: Article is behind a paywall.)

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