Better statistical reporting does not lead to statistical rigour: lessons from two decades of pseudoreplication in mouse-model studies of neurological disorders

Abstract Background Accurately determining the sample size (“N”) of a dataset is a key consideration for experimental design. Misidentification of sample size can lead to pseudoreplication, a process of artificially inflating the number of experimental replicates which systematically underestimates...

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主要な著者:	Constantinos Eleftheriou, Sarah Giachetti, Raven Hickson, Laura Kamnioti-Dumont, Robert Templaar, Alina Aaltonen, Eleni Tsoukala, Nawon Kim, Lysandra Fryer-Petridis, Chloe Henley, Ceren Erdem, Emma Wilson, Beatriz Maio, Jingjing Ye, Jessica C. Pierce, Kath Mazur, Lucia Landa-Navarro, Nina G. Petrović, Sarah Bendova, Hanan Woods, Manuela Rizzi, Vanesa Salazar-Sanchez, Natasha Anstey, Antonios Asiminas, Shinjini Basu, Sam A. Booker, Anjanette Harris, Sam Heyes, Adam Jackson, Alex Crocker-Buque, Aoife C. McMahon, Sally M. Till, Lasani S. Wijetunge, David JA Wyllie, Catherine M. Abbott, Timothy O’Leary, Peter C. Kind
フォーマット:	Artigo
言語:	Inglês
出版事項:	BMC 2025-05-01
シリーズ:	Molecular Autism
主題:	Autism Pseudoreplication Statistics Animal models Fragile X
オンライン･アクセス:	https://doi.org/10.1186/s13229-025-00663-3
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