AI Article Synopsis
- Null hypothesis significance testing (NHST) has faced criticism for its numerous issues, prompting researchers to explore alternatives like Bayes factors for hypothesis testing.
- The article introduces "null hypothesis Bayesian testing" (NHBT) as a method using Bayes factors but identifies several limitations and misinterpretations related to this approach through reproducible examples.
- The authors advocate for a greater focus on posterior model probabilities over Bayes factors, as they offer more direct answers to common research questions.
Article Abstract
Null hypothesis significance testing (NHST) has been under scrutiny for decades. The literature shows overwhelming evidence of a large range of problems affecting NHST. One of the proposed alternatives to NHST is using Bayes factors instead of p values. Here we denote the method of using Bayes factors to test point null models as "null hypothesis Bayesian testing" (NHBT). In this article we offer a wide overview of potential issues (limitations or sources of misinterpretation) with NHBT which is currently missing in the literature. We illustrate many of the shortcomings of NHBT by means of reproducible examples. The article concludes with a discussion of NHBT in particular and testing in general. In particular, we argue that posterior model probabilities should be given more emphasis than Bayes factors, because only the former provide direct answers to the most common research questions under consideration. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1037/met0000221 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evaluation of Bayesian Linear Regression derived gene set test methods.
BMC Genomics
December 2024
Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
Background: Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations.
Results: Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations.
Obesity, laypeople's beliefs and implications for clinicians and leaders of healthcare organisations.
BMJ Lead
December 2024
Faculty of Management, Bayes Business School, City University of London, London, UK
Background/aim: Overweight and obesity (OAO) is a major and growing public health crisis in the world. There is convincing medical evidence that caloric overconsumption, rather than lack of exercise, is the primary driver of OAO.
Methods: In this translation piece, we summarise our programme of research on laypeople's beliefs about the primary cause of OAO, the origins of these beliefs and implications for clinicians and leadership in healthcare organisations.
Identifying potential drug targets for myocardial infarction through Mendelian randomization.
PLoS One
December 2024
Department of Cardiology, Shaanxi Provincial People's Hospital, Xi'an, P. R. China.
Background: This study explored the associations between plasma and cerebrospinal fluid (CSF) proteins and myocardial infarction (MI) risk. Identifying specific proteins as biomarkers for MI could enhance our understanding of disease mechanisms and inform clinical practice.
Methods: We combined protein quantitative trait loci (pQTL) data for plasma and CSF proteins with genome-wide association study (GWAS) summary statistics for MI.
Understanding the Effectiveness of Water, Sanitation, and Hygiene Interventions: A Counterfactual Simulation Approach to Generalizing the Outcomes of Intervention Trials.
Environ Health Perspect
December 2024
Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Background: While water, sanitation, and hygiene (WASH) interventions can reduce diarrheal disease, many large-scale trials have not found the expected health gains for young children in low-resource settings. Evidence-based guidance is needed to improve interventions and remove barriers to diarrheal disease reduction.
Objectives: We aimed to estimate how sensitive WASH intervention effectiveness was to underlying contextual and intervention factors in the WASH Benefits (WASH-B) Bangladesh cluster-randomized controlled trial.
Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants.
Redox Biol
December 2024
The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China; Guangdong Clinical Research Academy of Chinese Medicine, Guangzhou, 510405, China. Electronic address:
Objective: To develop and validate a machine learning model incorporating dietary antioxidants to predict cardiovascular disease (CVD)-cancer comorbidity and to elucidate the role of antioxidants in disease prediction.
Methods: Data were sourced from the National Health and Nutrition Examination Survey. Antioxidants, including vitamins, minerals, and polyphenols, were selected as key features.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!