(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Felipe Nunes writes:
I have many friends working with data that they claim to be considered as a ‘population’. For example, the universe of bills presented in a Congress, the roll call votes of all deputies in a legislature, a survey with all deputies in a country, the outcomes of an election, or the set of electoral institutions around the world. Because of the nature of these data, we do not know how to interpret the p-value. I have seen many arguments been made, but I have never seen a formal response to the question. So I don’t know what to say. The most common arguments among the community of young researchers in Brazil are: (1) don’t interpret p-value when you have population, but don’t infer anything either; (2) interpret the p-value because of error measurement which is also present, (3) there is no such a thing as a population, so always look at p-values, (4) don’t worry about p-value, interpret the coefficients substantively, and (5) if you are frequentist you interpret p-value, if you are bayesian you don’t.
If you have a paper or any other reference that can help with this discussion, please refer to me as well.
Here’s my reply.
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