Definition: When there is a lack of statistical independence presented in the data and thus artificially inflating the number of samples (i.e. replicates). For instance, collecting more than one data point from the same experimental unit (e.g. participant or crops). Numerous methods can overcome this, such as averaging across replicates (e.g., taking the mean RT for a participant) or implementing mixed effects models with the random effects structure accounting for the pseudoreplication (e.g., specifying each individual RT as belonging to the same subject). Note, the former option would be associated with a loss of information and statistical power.

Related terms: Confounding, <a href='/glossary/generalizability/'>Generalizability</a>, Replication, <a href='/glossary/validity/'>Validity</a>

References: Davies and Gray (2015), Hurlbert (1984), & Lazic (2019)

Drafted and Reviewed by: Ben Farrar, Jamie P. Cockcroft, Mahmoud Elsherif, Elias Garcia-Pelegrin, Annalise A. LaPlume

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