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Going to extremes: the Goldilocks/Lagom principle and data distribution
Henry J Leese1, Thozhukat Sathyapalan2, Victoria Allgar3
1Centre for Atherothrombosis and Metabolic Disease, Hull York Medical School, University of Hull, Hull, UK henry.leese@hyms.ac.uk.
BMJ Open
|November 30, 2019
Summary
Presenting individual data values, not just averages, is crucial in biology and medicine. This approach helps identify optimal biological ranges, known as "Goldilocks" or "Lagom" effects, improving medical understanding and patient care.
Area of Science:
- Biology
- Medicine
- Biostatistics
Background:
- Biological and medical data are often summarized by mean/median with error bars, omitting individual data points.
- This common practice risks obscuring 'Goldilocks' or 'Lagom' effects, where optimal function occurs within a specific range of a biological variable.
Purpose of the Study:
- To highlight the importance of individual data values in biological and medical research.
- To investigate the prevalence and implications of 'Goldilocks'/'Lagom' phenomena in scientific literature.
Main Methods:
- Analysis of existing biological and medical datasets.
- Narrative literature search using the PubMed database to identify studies exhibiting 'Goldilocks'/'Lagom' patterns.
Main Results:
- The study confirmed that summarizing data by averages can hide crucial 'Goldilocks'/'Lagom' effects.
- A literature search revealed numerous quantitative and qualitative examples of these phenomena across health and social sciences.
Conclusions:
- Retrospective analysis of data is likely to uncover many more 'Goldilocks'/'Lagom' distributions, enhancing medical insights.
- Adopting a transparent method of presenting individual data values is recommended for comprehensive research evaluation and improved clinical care.
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