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small samples

Papers that caught my attention last week

Berk Ozler's picture
A lot of interesting papers came across my desk this past week. Here are a few that I think may be useful to you too (and why):

Practical advice on robust standard errors in (not so small) samples: Imbens and Kolesár have an old working paper just published in REStat that tells you to do three things:

Be an Optimista, not a Randomista (when you have small samples)

Berk Ozler's picture
We are often in a world where we are allowed to randomly assign a treatment to assess its efficacy, but the number of subjects available for the study is small. This could be because the treatment (and its study) is very expensive – often the case in medical experiments – or because the condition we’re trying to treat is rare leaving us with two few subjects or because the units we’re trying to treat are like districts or hospitals, of which there are only so many in the country/region of interest.