Dear Statalisters:
I am revising an article for publication. I have data for the entire population I am studying. Nonetheless, I employ inferential statistics. Specifically, to analyze the data I used a multivariate Poisson regression model (several actually) for hypothesis testing. One of the reviewers asked the obvious question: Why did you use inferential statistics when you have data for the entire population? I have read discussions about this topic previously on this list, and I have a pretty clear idea in my head of why using inferential statistics still makes sense when your sample is the entire population.
I am writing now with a simple question: Do you think an explanation that reads like this is appropriate and/or sufficient to deal with the reviewer?s point?
?As I mention above, the data we utilize here come from the full population under study rather than a sample of the population. This, of course, begs the following question: Why do we use inferential statistics? Our answer is twofold. First, the models we estimate subsequently almost certainly are imperfect in the sense that they do not contain all possible predictors. In other words, our models are by definition approximations of reality rather than complete and accurate representations of reality. This makes inferential statistics appropriate. Second, inferential statistics account for error. Perhaps most important, they account for sampling error. But they also account for measurement error, which almost surely is present here (as it is everywhere in the social sciences). In short, though our data come from the entire population rather than a sample, we believe that inferential statistics are appropriate for our purposes.?
I would appreciate any feedback or advice.
Antonio Silva
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