The statistical power of three monkeys
- Neuroscience studies in non-human primates (NHP) often follow the rule of thumb that results observed in one animal must be replicated in at least one other. However, we lack a statistical justification for this rule of thumb, or an analysis of whether including three or more animals is better than including two. Yet, a formal statistical framework for experiments with few subjects would be crucial for experimental design, ethical justification, and data analysis. Also, including three or four animals in a study creates the possibility that the results observed in one animal will differ from those observed in the others: we need a statistically justified rule to resolve such situations. Here, I present a statistical framework to address these issues. This framework assumes that conducting an experiment will produce a similar result for a large proportion of the population (termed ‘representative’), but will produce spurious results for a substantial proportion of animals (termed ‘outliers’); the fractions of ‘representative’ and ‘outliers’ animals being defined by a prior distribution. I propose a procedure in which experimenters collect results from M animals and accept results that are observed in at least N of them (‘N-out-of-M’ procedure). I show how to compute the risks α (of reaching an incorrect conclusion) and β (of failing to reach a conclusion) for any prior distribution, and as a function of N and M. Strikingly, I find that the N-out-of-M model leads to a similar conclusion across a wide range of prior distributions: recordings from two animals lowers the risk α and therefore ensures reliable result, but leaves a large risk β; and recordings from three animals and accepting results observed in two of them strikes an efficient balance between acceptable risks α and β. This framework gives a formal justification for the rule of thumb of using at least two animals in NHP studies, suggests that recording from three animals when possible markedly improves statistical power, provides a statistical solution for situations where results are not consistent between all animals, and may apply to other types of studies involving few animals.
Author: | Jean LaurensORCiD |
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URN: | urn:nbn:de:hebis:30:3-730533 |
DOI: | https://doi.org/10.1101/2022.05.10.491373 |
Parent Title (English): | bioRxiv |
Document Type: | Preprint |
Language: | English |
Date of Publication (online): | 2022/05/10 |
Date of first Publication: | 2022/05/10 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2023/03/20 |
Issue: | 2022.05.10.491373 |
Page Number: | 13 |
HeBIS-PPN: | 506717208 |
Dewey Decimal Classification: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International |