This week, yet again, I was confronted by yet another facet of the nonsensical nature of the frequentist approach to statistics. The blog of Andrew Gelman drew my attention to a recent peer-reviewed paper studying the extent of misunderstanding of the meaning of confidence intervals, among students and researchers. What shocked me, though, was not the only findings of the study.

Confidence intervals are a relatively simple idea in statistics, used to quantify the precision of a measurement. When a measurement is subject to statistical noise, the result is not going to be exactly equal to the parameter under investigation. For a high quality measurement, where the impact of the noise is relatively low, we can expect the result of the measurement to be close to the true value. We can express this expected closeness to the truth by supplying a narrow confidence interval. If the noise is more dominant, then the confidence interval will be wider - we will be less sure that truth is close to the result of the measurement. Confidence intervals are also known as error bars.