ArticleEvolution letters2020
Controlling for body size leads to inferential biases in the biological sciences.
Article in Evolution letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Socioecological complexity in primate groups and its cognitive correlates.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Pooled it
- Article
- Slowly but Surely: Larger Brains Improve Immature Survival in Primates.American journal of primatology · 2025Article
- Beyond the mosaic model of brain evolution: Rearing environment defines local and global plasticity.Annals of the New York Academy of Sciences · 2024Article
- Horn size is linked to Sertoli cell efficiency and sperm size homogeneity during sexual development in common eland (Frontiers in cell and developmental biology · 2024Article
- Body size predicts the rate of contemporary morphological change in birds.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Using relative brain size as predictor variable: Serious pitfalls and solutions.Ecology and evolution · 2022Article
- Causation, not collinearity: Identifying sources of bias when modelling the evolution of brain size and other allometric traits.Evolution letters · 2022Article
- Brain size and neuron numbers drive differences in yawn duration across mammals and birds.Communications biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many traits correlate with body size. Studies that seek to uncover the ecological factors that drive evolutionary responses in traits typically examine these responses relative to associated changes in body size using multiple regression analysis. However, it is not well appreciated that in the presence of strongly correlated variables, the partial (i.e., relative) regression coefficients often change sign compared to the original coefficients. Such sign reversals are difficult to interpret in a biologically meaningful way, and could lead to erroneous evolutionary inferences if the true mechanism underlying the sign reversal differed from the proposed mechanism. Here, we use simulations to demonstrate that sign reversal occurs over a wide range of parameter values common in the biological sciences. Further, as a case-in-point, we review the literature on brain size evolution; a field that explores how ecological traits relate to the evolution of relative brain size (brain size relative to body size). We find that most studies show sign reversals and thus that the inferences of many studies in this field may be inconclusive. Finally, we propose some approaches to mitigating this issue.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.