Confidence interval (CI)

It is «a range of values, calculated from the sample observations, that is believed, with a particular probability, to contain the true parameter value» [1]. It is used «to express the degree of uncertainty associated with a sample statistic. A confidence interval is an interval estimate combined with a probability statement.» One «might describe the interval estimate as a "95% confidence interval". This means that if» one «used the same sampling method to select different samples and computed an interval estimate for each sample,» one «would expect the true population parameter to fall within the interval estimates 95% of the time» [2]. «Precision is taken to be the narrowness of the confidence interval. (...) The interval estimate is an expression of the uncertainty surrounding the point estimate and derives mainly from sampling variation as well as measurement variation/error. In general, the degree of uncertainty is inversely related to the size of the study. On one hand, if a study is too small, the uncertainty may increase to a level considered to be undesirable or useless. On the other, as the study size increases, the degree of uncertainty decreases, and the interval estimate becomes narrower» [3]. «Confidence intervals are preferred to point estimates and to interval estimates, because only confidence intervals indicate the precision of the estimate and the uncertainty of the estimate» [2].

Bibliographic references:

[1] Everitt, B. and Skrondal, A. (2010). The Cambridge dictionary of statistics. 4th ed. Cambridge, UK: Cambridge University Press.

[2] Stattrek.com. (2016). Statistics Dictionary. [online] Available at: http://stattrek.com/statistics/dictionary.aspx [Accessed 13 Nov. 2016].

[3] Broeck, J. and Brestoff, J. (2013). Epidemiology: Principles and Practical Guidelines. 1st ed. Dordrecht: Springer.