Kino Zhao

This article presents a perspectivist framework for understanding and evaluating statistical assumptions. Drawing on the thesis of perspectivism from the philosophy of science, this framework treats statistical assumptions not as empirical hypotheses that are descriptively accurate or inaccurate about the world but as prescribing a particular perspective from which statistical knowledge is generated. What this means is that we ought not judge statistical models solely by how closely they correspond with the world as we independently understand it, but by whether they paint a picture of the world that is epistemically significant.

This paper examines the role existence plays in measurement validity. I argue that existing popular theories of measurement and of validity follow a correspondence framework, which starts by assuming that an entity exists in the real world with certain properties that allow it to be measurable. Drawing on literature from the sociology of measurement, I show that the correspondence framework faces several theoretical and practical challenges. I suggested the validity-first framework of measurement, which starts with a practice-based validation process as the basis for a measurement theory, and only posits objective existence when it is scientifically useful to do so.

The social sciences face a problem of sample non-representation, where the majority of samples consist of undergraduate students from Euro-American institutions. The problem has been identified for decades with little trend of improvement. In this paper, I trace the history of sampling theory. The dominant framework, called the design-based approach, takes random sampling as the gold standard. The idea is that a sampling procedure that is maximally uninformative prevents samplers from introducing arbitrary bias, thus preserving sample representation. I show how this framework, while good in theory, faces many challenges in application. Instead, I advocate for an alternative framework, called the model-based approach to sampling, where representative samples are those balanced in composition, however they were drawn. I argue that the model-based framework is more appropriate in the social sciences because it allows for systematic assessment of imperfect samples and methodical improvement in resource-limited scientific contexts. I end with practical proposals of improving sample quality in the social sciences.

At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are in a situation where the VC theorem can be applied for the purpose we want it to.