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Equine veterinary journal2020; 53(2); 199-204; doi: 10.1111/evj.13351

Study design synopsis: Battle in the stable: Bayesianism versus Frequentism.

Abstract: Frequentism dominates scientific practice although Bayesianism may provide an alternative, especially when analysing data from complex, high-dimensional models. The key differences between Bayesianism and Frequentism are highlighted in the introduction. Next, I review the different stages of Bayesian statistical reasoning in a research setting, explain the key concepts and illustrate them with toy examples taken in equine veterinary medicine. An extension to more complex models (Bayes network) is introduced and guidelines are offered as a conclusion.
© 2020 EVJ Ltd.
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Publication Date: 2020-11-02 PubMed ID: 33136311DOI: 10.1111/evj.13351Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article explores the contrasting statistical methods of frequentism and Bayesianism, particularly their application in analyzing complex, high-dimensional models in scientific research settings, using examples from equine veterinary medicine.

Principal Distinctions Between Bayesianism and Frequentism

The article initially distinguishes between the two prevalent statistical methods, Bayesianism and frequentism. These methods serve different purposes in statistical inferences and analyses:

  • Frequentism relies on long-term frequencies and assumes probabilities as objective, fixed quantities that reflect the frequency of events.
  • Bayesianism, on the other hand, treats probabilities as subjective belief or knowledge about uncertain events that are updated as new evidence becomes available.

Bayesian Statistical Reasoning

After establishing the key differences, the paper delves into the Bayesian statistical reasoning process, particularly as it applies within a research setting.

  • The process starts with prior belief about the probability of an event, known as the prior distribution.
  • New data are invoked to update these prior beliefs, leading to the so-called posterior distribution which reflects updated knowledge about the event.
  • The article illuminates this intricate process through “toy” examples sourced from the field of equine veterinary medicine. The examples help simplify and illustrate the Bayesian process for readers.

Extension to Complex Models

The author then shows how the Bayesian approach can be extended to more intricate models, such as Bayes network.

  • Bayes networks are a sophisticated kind of model that uses a graphical approach to display relationships among several variables.
  • The extension is an attempt to clarify possible misconceptions and showcase how the Bayesian approach can be effective in handling highly dimensional models.

Concluding Guidelines

The article concludes by providing guidelines to help researchers apply these principles in their work. This serves to help readers to better understand the two different statistical methods and to assist them when choosing between them for conducting their research.

Cite This Article

APA
Detilleux J. (2020). Study design synopsis: Battle in the stable: Bayesianism versus Frequentism. Equine Vet J, 53(2), 199-204. https://doi.org/10.1111/evj.13351

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 53
Issue: 2
Pages: 199-204

Researcher Affiliations

Detilleux, Johann
  • Farah - Productions durables, Universtity of Liege, Liege, Belgium.

MeSH Terms

  • Animals
  • Bayes Theorem
  • Horses
  • Research Design

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