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A Bayesian predictive approach for dealing with pseudoreplication

Research output: Contribution to journalArticle

Original languageEnglish
Article number2366 (2020)
Number of pages10
JournalScientific Reports
DateAccepted/In press - 28 Jan 2020
DatePublished (current) - 11 Feb 2020


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.

    Structured keywords

  • Physical and Mental Health
  • Brain and Behaviour
  • Cognitive Neuroscience

    Research areas

  • cellular neuroscience, statistical methods

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