P values?
Do they account solely for sampling error (therefore irrelevant when population data is available) OR do they serve to asses the likelihood of something being due to chance in other ways (therefore relevant for studies with population data)?
Any links or literature are welcome :)
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Look up super-population theory. It is based on the idea that even a perfect census is only a point-in-time estimate of the theoretical “super-population” that the point-in-time population is derived from. In large, real world populations, people are constantly coming and going. If we assume that this coming and going is random and the relevant super-population parameters do not change over time, it is easy to see a census population as a sample instance from a larger super-population. While somewhat theoretical, this is a useful model when estimating relationships between variables in census data and leads to the use of standard frequentist confidence intervals and, yes, even p-values.
@sailingbythelee I’ve seen this argument around, but not with reference to a formalized theory. Now I’ve got something to look up and see if it makes sense for my research, thank you so much!
That’s a very cool way to look at it. You’re basically taking “a sample in time” and will never be able to sample across time (assuming we don’t invent time machines… ever), so you will always be looking at a super-population that is technically infinite. =)