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Table 1 Consider the following two examples of a binary outcome with complete population coverage

From: What’s more general than a whole population?

Presidential election: there is 100% turnout in a national presidential election, with each vote being either for candidate A or candidate B.
Cancer registry: male or female sex is registered for all cases in a national registry which has 100% coverage of the type of cancer in question.
In the election example, is it meaningful to estimate a sampling error for the proportion voting for candidate A? The answer seems to be clearly ‘no’. This is because the purpose of the election is to choose a president, which is done on the basis of the observed proportion of votes cast. Any kind of interval estimate serves no purpose because there is no generalizability beyond the election.
In the cancer registry example, is it meaningful to estimate a sampling error for the proportion of cases who are female? Some would say ‘no’ on the basis that it’s a complete population enumeration with no sampling error. Similar examples in the literature show that some authors would say ‘yes’. This implies an attempt to generalize beyond the population observed, but what is this wider target population? Conceivably future cases, or a wider, supranational geographical area, although often this is left unspecified.