The Power of Microbiome Studies: Some Considerations on Which Alpha and Beta Metrics to Use and How to Report Results
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Abstract
Background: Since sequencing techniques have become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observe dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data were used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota datasets and the effect on the sample size. Results: Our data showed that beta diversity metrics are the most sensitive to observe differences as compared with alpha diversity metrics. The structure of the data influenced which alpha metrics are the most sensitive. Regarding beta diversity, the Bray–Curtis metric is in general the most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias. Conclusion: We recommend performing power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies, awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: because of this, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e., p -value < α. This way of proceeding is one of the many forms of the so-called p -value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p -hacking would be to publish a statistical plan before experiments are initiated, describing the outcomes of interest and the corresponding statistical analyses to be performed.
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Data interpretation must be cautious due to high inter-individual variability, lack of standardization in pipelines, and the absence of a universally defined healthy microbiome.
“Data interpretation must be cautious due to high inter-individual variability, lack of standardization in pipelines, and the absence of a universally defined healthy microbiome [76,77].”
From The Seven Methods for the Evaluation of Nutritional Status—ABCDEFG: Narrative Review (2026) · cites this paper as [76]
The Discussion (page unavailable) cautions that the choice of alpha and beta diversity metrics can affect findings and that incomplete reporting makes results difficult to judge. The paper does not identify high inter-individual variability, lack of pipeline standardization, or the absence of a universally defined healthy microbiome as the reasons for caution stated in the citing sentence. (LLM, conf=0.86)
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Microbiome and Long COVID-19: Current Evidence and Insights
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Fatty Acids as Prebiotics and Their Role in Antibiofilm Activity
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Gut Dysbiosis: A New Avenue for Stroke Prevention and Therapeutics
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Influence of the gut and airway microbiome on asthma development and disease
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The Microbiome–Neurodegeneration Interface: Mechanisms, Evidence, and Future Directions
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Gut Microbiota and Metabolic Health: From Dysbiosis to Therapeutics
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Probiotics and eating disorders: a systematic review of humans and animal model studies
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Gut microbiome composition and dysbiosis in immune thrombocytopenia: A review of literature
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Effects of Postbiotic Administration on Canine Health: A Systematic Review and Meta-Analysis
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Machine Learning Approaches to Assess Soil Microbiome Dynamics and Bio‐Sustainability
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Exercise and the gut microbiome: implications for supportive care in cancer
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Effects of humic acid application on bacterial diversity under maize cultivation
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Dual stressors of infection and warming can destabilize host microbiomes
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- Authors
- Kers, J. G., & Saccenti, E.
- Affiliations
- Wageningen University & Research (NL)
- Corresponding author
- Saccenti, Edoardo
- Venue
- Frontiers in Microbiology
- Publisher
- Frontiers Media SA
- Published
- 2022-03-03
- Volume
- 12
- Pages
- 796025-796025
- ISSN
- 1664-302X (ISSN-L)
- PMC
- PMC8928147
- OpenAlex
- W4214875183
- Cited by
- 359
- References
- 70
- Citations by year
- 2026: 121 · 2025: 121 · 2024: 81 · 2023: 31 · 2022: 5
- FWCI
- 19.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Microbiology (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- European Commission
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Keywords
- microbiome, sample size determination, statistical power, 16S rRNA amplicon sequencing, Bray-Curtis distance, p-hacking, publication bias, statistical reporting, pre-analysis plan
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