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An Efficient Single—Person Technique for Milk Sampling from Laboratory Mice
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Student and the Lanarkshire milk experiment.

Stephen Senn1

  • 1School of Health and Related Research, University of Sheffield, Sheffield, UK. stephen@senns.uk.

European Journal of Epidemiology
|December 8, 2022
PubMed
Summary

This study re-examines the 1930 Lanarkshire Milk Experiment, highlighting statistical insights applicable to modern experimental design and observational epidemiology. It emphasizes appropriate standard error estimation and potential hidden clustering effects.

Keywords:
Cluster designIncomplete blocksNutritionRandom effectsRandomisationStandard errorsStudent

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Area of Science:

  • Statistics
  • Epidemiology
  • Experimental Design

Background:

  • The 1930 Lanarkshire Milk Experiment (LME) investigated the effects of raw vs. pasteurized milk.
  • William Sealy Gossett ("Student") provided a statistical critique in 1931.
  • The LME involved a complex design with school and pupil-level allocation.

Purpose of the Study:

  • To re-examine the 1930 Lanarkshire Milk Experiment from a modern statistical perspective.
  • To evaluate "Student's" criticisms in light of contemporary experimental design.
  • To draw lessons for observational studies in epidemiology.

Main Methods:

  • Re-analysis of the 1930 Lanarkshire Milk Experiment's design and statistical approach.
  • Examination of "Student's" criticisms concerning incomplete block structures.
  • Analogy drawn with modern clinical trials, such as in osteoarthritis research.

Main Results:

  • "Student's" criticisms are assessed based on advancements in experimental design and analysis.
  • The importance of appropriate standard error estimation in complex experimental structures is highlighted.
  • Potential explanations for variability in observational studies, including hidden clustering, are discussed.

Conclusions:

  • The LME's complex design offers enduring lessons for statistical analysis.
  • Modern statistical methods can provide deeper insights into historical experiments.
  • Hidden clustering may explain inconsistencies in observational epidemiological studies.