English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Why Confidence Intervals Should be Used in Reporting Studies of Complete Populations