Beyond subjective and objective in statistics

By Andrew Gelman, Christian Hennig. 2015

217 citations recorded by the source corpus.

Decisions in statistical data analysis are often justified, criticized or avoided by using concepts of objectivity and subjectivity. We argue that the words ‘objective’ and ‘subjective’ in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability, these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. The advantage of these reformulations is that the replacement terms do not oppose each other and that they give more specific guidance about what statistical science strives to achieve. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgement of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling and socio‐economic stratification. The aim of the paper is to push users and developers of statistical methods towards more effective use of diverse sources of information and more open acknowledgement of assumptions and goals.

Subfields: Experimental and Cognitive Psychology, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/226d10ff2496e910904502f7d10a2b150f0e466f

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Citations in the corpus

How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset (2018, background): “…and objectivity in statistical modelling, and how these concepts can be substituted by concepts such as transparency, consensus, impartiality and correspondence to observable reality, awareness of multiple perspectives, context dependence and stability can be found in Gelman and Hennig (2017).”
Best practice guidance for linear mixed-effects models in psychological science (2019, background): “In a helpful recent discussion, Gelman and Hennig (2017) explore the ways in which researchers can usefully move to considering statistical analyses in terms of transparency, consensus, impartiality,”
The Prior Can Often Only Be Understood in the Context of the Likelihood (2017, background): “These different roles often motivate a distinction between “subjective” and “objective” choices of priors, but we are unconvinced of the relevance of this distinction [1].”
Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication (2018, background): “Because all statistical methods require subjective choices (Gelman and Hennig 2017), there is no objective decision machine for automatic scientific inference; it must be we who make the inference, and claims about a larger population will always be uncertain.”