Monday , December 4 2023
Home / Lars P. Syll / Statistical reification — a deadly sin

# Statistical reification — a deadly sin

Summary:
Statistical reification — a deadly sin It would not be an exaggeration to say that many of us see academic statistics as “the sick man of science,” an alarming diagnosis in light of the central role it plays in science in general. The situation is maintained through specious and often byzantine rationales for destructive traditions, which is as expected from another human condition: Blindness to conceptual mistakes when correcting those would threaten the entire foundation on which one’s teaching and research (which is to say, entire careers) have been built. The central example is how, by themselves, P-values and CI do not test any hypothesis, nor do they measure the significance of results or the confidence we should have in them. The sense otherwise

Topics:
Lars Pålsson Syll considers the following as important:

This could be interesting, too:

Lars Pålsson Syll writes Improving econometric education

Lars Pålsson Syll writes Why we need causality in science

Lars Pålsson Syll writes Improving econometric analysis

Lars Pålsson Syll writes Guido Imbens on the response to LATE

## Statistical reification — a deadly sin

It would not be an exaggeration to say that many of us see academic statistics as “the sick man of science,” an alarming diagnosis in light of the central role it plays in science in general. The situation is maintained through specious and often byzantine rationales for destructive traditions, which is as expected from another human condition: Blindness to conceptual mistakes when correcting those would threaten the entire foundation on which one’s teaching and research
(which is to say, entire careers) have been built. The central example is how, by themselves, P-values and CI do not test any hypothesis, nor do they measure the significance of results or the confidence we should have in them. The sense otherwise is an ongoing cultural error perpetuated by large segments of the statistical and research community via evocative terminology such as “statistical significance” and “confidence interval” for what are only dichotomous P-value summaries …

P-values and CIs can only become contextually interpretable when derived explicitly from causal stories about the real data generator (such as randomization), and can only become reliable when those stories are based on valid and public documentation of the physical mechanisms that generated the data. Absent these assurances, traditional interpretations of “inferential statistics” become pernicious fictions that need to be replaced by far more circumspect descriptions of data and model relations.

Sander Greenland

Professor at Malmö University. Primary research interest - the philosophy, history and methodology of economics.