Biological phenomena consistently follow Gaussian statistics
While the normal or Gaussian distribution is frequently applied in biostatistics and modeling, many biological phenomena are better characterized by alternative distributions such as power laws or skewed models, meaning they do not consistently follow Gaussian statistics.
The claim asserts that biological phenomena consistently follow Gaussian statistics. While several papers utilize Gaussian models for specific biological systems or note the historical prevalence of normal distributions in biostatistics (e.g., Papers 1, 2, and 8), other prominent works emphasize that many natural and biological systems are actually governed by power laws, skewed distributions, or alternative models where Gaussian assumptions fail (e.g., Papers 3, 4, 5, 6, and 11). This demonstrates genuine contestation rather than universal consistency, leading to a verdict of CONTESTED.
V. Purutçuoğlu, H. Farnoudkia. Copula Gaussian graphical modelling of biological networks and Bayesian inference of model parameters. 2019. https://doi.org/10.24200/SCI.2019.5071.1076
Paper 1 uses Copula Gaussian graphical models to successfully represent complex biological networks and datasets.
Z. Ma. Coupling Power Laws Offers a Powerful Modeling Approach to Certain Prediction/Estimation Problems With Quantified Uncertainty. 2022. https://doi.org/10.3389/fams.2022.801830
Paper 3 notes that power law distributions frequently fit natural and biological data exceptionally well when the Gaussian distribution fails.
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Luís C B Silva, Bruna Lopes, Isidro Blanquet, C. Marques. Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming. 2021. https://doi.org/10.3390/app11135875
Paper 2 demonstrates a Gaussian distribution model for monitoring biological and chemical systems in aquaculture.
Sangseok Lee. Escape from omnishambles in statistics: back to the basics. 2015. https://doi.org/10.4097/kjae.2015.68.5.431
Paper 8 notes that many biological phenomena and biostatistics are traditionally modeled using normal distributions.
Z. Ma. Coupling Power Laws Offers a Powerful Method for Problems such as Biodiversity and COVID-19 Fatality Predictions. 2021
Paper 4 states that power law distributions are often found to fit biological data when normal distributions fail.
Leigh J. Halliwell. The Log-Gamma Distribution and Non-Normal Error. 2021. https://doi.org/10.66573/001c.140802
Paper 5 shows that biological or actuarial error terms are frequently skewed and non-Gaussian.
Miguel Franco. The time distribution of biological phenomena – illustrated with the London marathon. 2018. https://doi.org/10.7287/peerj.preprints.27175v1
Paper 6 points out that existing standard distributions are often inappropriate for biological phenomena because biological dynamics possess unique statistical properties.
Hongxiang Li, Tsung Fei Khang. clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution. 2022. https://doi.org/10.7717/peerj.16126
Paper 11 utilizes a skew-normal distribution to model RNA-Seq gene counts, demonstrating that non-standard distributions are needed for biological variability.
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