I think that your statement below makes two errors of statistical logic. Inferential statistics has nothing to do with omitted variable bias. Nor will it take measurement error in your variables into account. There is a debate going back into the 1960s (if not earlier) as to whether it makes sense to use inferential statistics when you have population data. In your case, where you are estimating Poisson regressions, you are positing a random process, and you might reasonably argue that this justifies the use of inferential statistics. David Greenberg, Sociology Department, New York University
----- Original Message -----
From: Antonio Silva <[email protected]>
Date: Friday, May 29, 2009 5:08 pm
Subject: st: Explaining the Use of Inferential Statistics Even Though I Have Population Data
To: Stata list <[email protected]>
> 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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