Abstract
Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and thus fully contributing to the result. By artificially inflating the sample size, pseudoreplication contributes to irreproducibility, and it is a pervasive problem in biological research. In some fields, more than half of published experiments have pseudoreplication – making it one of the biggest threats to inferential validity. Researchers may be reluctant to use appropriate statistical methods if their hypothesis is about the pseudoreplicates and not the genuine replicates; for example, when an intervention is applied to pregnant female rodents (genuine replicates) but the hypothesis is about the effect on the multiple offspring (pseudoreplicates). We propose using a Bayesian predictive approach, which enables researchers to make valid inferences about biological entities of interest, even if they are pseudoreplicates, and show the benefits of this approach using two in vivo data sets.
Highlights
Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and fully contributing to the result
Statisticians and quantitative biologists have worried about pseudoreplication – known as the unit-of-anlysis problem[1]
The controversy may persist because formulating a scientific question as statistical model can be difficult if one has received little formal training in experimental design or statistics – like many laboratory-based biologists
Summary
Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and fully contributing to the result. The two main ways of dealing with pseudoreplication are: (1) average the pseudoreplicates to obtain one value per genuine replicate, or (2) use a more sophisticated approach that captures the structure of the data where the pseudoreplicates are nested under the genuine replicates, such as a multilevel/hierarchical model[8,9,10,11]. These recommended methods have lead to a key point of contention. There is a renewed interest in prediction[16,17,18,19,20], perhaps due to the attention that machine learning is receiving
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