Manipulating the alpha level cannot cure significance testing

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are probl...

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Detalles Bibliográficos
Autores: Trafimow, David, Amrhein, Valentin, Areshenkoff, Corson N., Barrera-Causil, Carlos J., Beh, Eric J., Bilgiç, Yusuf K., Bono Cabré, Roser, Bradley, Michael T., Briggs, William M., Cepeda-Freyre, Héctor A., Chaigneau, Sergio E., Ciocca, Daniel R., Correa, Juan Carlos, Cousineau, Denis, de Boer, Michiel R., Dhar, Subhra Sankar, Dolgov, Igor, Gómez Benito, Juana, Grendar, Marian, Grice, James W., Guerrero Giménez, Martín E., Gutiérrez, Andrés, Huedo-Medina, Tania B., Jaffe, Klaus, Janyan, Armina, Karimnezhad, Ali, Korner-Nievergelt, Fränzi, Kosugi, Koji, Lachmair, Martin, Ledesma, Rubén D., Limongi, Roberto, Liuzza, Marco Tullio, Lombardo, Rosaria, Marks, Michael J., Meinlschmidt, Gunther, Nalborczyk, Ladislas, Nguyen, Hung T., Ospina, Raydonal, Pérez-González, José D.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/144500
Acceso en línea:https://hdl.handle.net/2445/144500
Access Level:acceso abierto
Palabra clave:Presa de decisions (Estadística)
Tests d'hipòtesi (Estadística)
Statistical decision
Statistical hypothesis testing
Descripción
Sumario:We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

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