RESEARCH ARTICLE
Why Confidence Intervals Should be Used in Reporting Studies of Complete Populations
Matthew D. Redelings1, Frank Sorvillo1, 2, Lisa V. Smith1, 2, *, Sander Greenland2, 3
Article Information
Identifiers and Pagination:
Year: 2012Volume: 5
First Page: 52
Last Page: 54
Publisher ID: TOPHJ-5-52
DOI: 10.2174/1874944501205010052
Article History:
Received Date: 28/05/2012Revision Received Date: 18/07/2012
Acceptance Date: 18/07/2012
Electronic publication date: 4/10/2012
Collection year: 2012
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Public-health reports sometimes leave out confidence intervals when data are presented for an entire popula-tion. A rationale cited for this practice is that population statistics are measurements rather than estimates; hence there is no need to consider random error because the statistics show exactly what occurred. We argue that this reason does not justify leaving out interval estimates. Targeting intervention in areas with high disease rates can be justified only on the assumption that the excess would continue in those areas; in that case, at the very least, we need to allow for random fluc-tuations over time. Thus, we recommend that interval estimates be reported even when the entire population is observed.