Common errors of interpretation in biostatistics

Elsa Vazquez Arreola, Kyle Irimata, Jeffrey R. Wilson

Research output: Contribution to journalArticlepeer-review

Abstract

What do we wish to investigate? While this may be a common question in research, it does not always come with straightforward answers. This article reviews data-driven methods of collection, questions asked and questions answered, and the myriad of different conclusions that may result. We examine differences in answers to questions based on independent versus correlated observations, bivariate versus conditional associations, relations versus extrapolation, and single membership versus multiple membership modeling. Regardless of the issue, these differences are usually not due to so-called bad data or due to bad models; they are usually due to the investigators misinterpreting the answers that were given. Most importantly, one cannot ask a question and obtain an answer without understanding the data structure, its size and its representativeness. Simply stated, the fact that I went to the store and bought an outfit does not mean the outfit is appropriate for the event. The answers obtained may not be answering the question of interest.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalBiostatistics and Epidemiology
DOIs
StatePublished - 2020

Keywords

  • Correlated data
  • hierarchical level data
  • hypotheses
  • logistic regression

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics

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