Understanding statistical principles in correlation, causation and moderation in human disease

Michael P Jones, Marjorie M Walker and John R Attia
Med J Aust 2017; 207 (3): . || doi: 10.5694/mja16.00697
Published online: 7 August 2017

We are all familiar with the expression “correlation does not imply causation”, but often causation is exactly what we need to determine. For example, one may want to understand whether the use of MP3 players with earbuds causes partial hearing loss, whether holding mobile telephones to the ear causes brain cancer or whether parents’ exposure to toxic chemicals during conception or pregnancy causes birth defects in children. Non-causal risk factors can be useful, but eventually, what we really want to understand is causation. Because the causal connection between exposure to risk factor and disease outcome is often complex or poorly understood, what researchers can truly study is whether an association exists or not. This article explains how we can move from correlation and association to causal interpretation of data, and what statistical evidence is needed to support causal conclusions.

  • Michael P Jones1
  • Marjorie M Walker2
  • John R Attia3,4

  • 1 Macquarie University, Sydney, NSW
  • 2 University of Newcastle, Newcastle, NSW
  • 3 Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, Newcastle, NSW
  • 4 John Hunter Hospital, Newcastle, NSW


Competing interests:

No relevant disclosures.


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