In the deep end: pooling data and other statistical challenges of zoo and aquarium research
Abstract 10.1002/zoo.20089.abs Zoo and aquarium research presents many logistic challenges, including extremely small sample sizes and lack of independent data points, which lend themselves to the misuse of statistics. Pseudoreplication and pooling of data are two statistical problems common in research in the biological sciences. Although the prevalence of these and other statistical miscues have […]
Pseudoreplication is a pseudoproblem
Pseudoreplication is one of the most influential methodological issues in ecological and animal behavior research today. At its inception, the idea of pseudoreplication highlighted important concerns about the design and analysis of experiments in ecology. The doctrine purported to provide a unified view of experimental design and analysis, wherein precise criteria could be used to […]
Remedies for pseudoreplication
Pseudoreplication is the failure of a statistical analysis to properly incorporate the true structure of randomness present in the data. It has been well documented and studied in the ecological literature but has received little attention in the fisheries literature. Avoiding pseudoreplication in analyses of fisheries data can be difficult due to the complexity of […]
Pseudoreplication: a widespread problem in primate communication research
Pseudoreplication (the pooling fallacy) is a widely acknowledged statistical error in the behavioural sciences. Taking a large number of data points from a small number of animals creates a false impression of a better representation of the population. Studies of communication may be particularly prone to artificially inflating the data set in this way, as […]
Taking the aggravation out of data aggregation: A conceptual guide to dealing with statistical issues related to the pooling of individual-level observational data
Field data often include multiple observations taken from the same individual. In order to avoid pseudoreplication, it is commonplace to aggregate data, generating a mean score per individual, and then using these aggregated data in subsequent analyses. Aggregation, however, can generate problems of its own. Not only does it lead to a loss of information, […]