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    Manipulating the alpha level cannot cure significance testing – comments on "Redefine statistical significance"
    (PeerJ Preprints, 2017-11-14) Trafimov D; Amrhein V; Areshenkoff CN; Barrera - Causil C; Beh EJ; Bilgiç Y; Bono R; Bradley MT; Briggs WM; Cepeda - Freyre HA; Chaigneau SE; Ciocca DR; Correa JC; Cousineau D; de Boer MR; Dhar SS; Dolgov I; Gómez - Benito J; Grendar M; Grice J; Guerrero - Gimenez ME; Gutiérrez A; Huedo - Medina TB; Jaffe K; Janyan A; Karimnezhad A; Korner - Nievergelt F; Kosugi K; Lachmair M; Ledesma R; Limongi R; Liuzza MT; Lombardo R; Marks M; Meinlschmidt G; Nalborczyk L; Nguyen HT; Ospina R; Perezgonzalez JD; Pfister R; Rahona JJ; Rodríguez - Medina DA; Romão X; Ruiz - Fernández S; Suarez I; Tegethoff M; Tejo M; van de Schoot R; Vankov I; Velasco - Forero S; Wang T; Yamada Y; Zoppino FCM; Marmolejo - Ramos F
    We argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable criterion levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and determining sample sizes much more directly than significance testing does; but none of the statistical tools should replace significance testing 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, or implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.
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    Levels of measurement and statistical analyses
    (24/05/2021) Williams M
    Most researchers and students in psychology learn of S. S. Stevens’ scales or “levels” of measurement (nominal, ordinal, interval, and ratio), and of his rules setting out which statistical analyses are admissible with each measurement level. Many are nevertheless left confused about the basis of these rules, and whether they should be rigidly followed. In this article, I attempt to provide an accessible explanation of the measurement-theoretic concerns that led Stevens to argue that certain types of analyses are inappropriate with data of particular levels of measurement. I explain how these measurement-theoretic concerns are distinct from the statistical assumptions underlying data analyses, which rarely include assumptions about levels of measurement. The level of measurement of observations can nevertheless have important implications for statistical assumptions. I conclude that researchers may find it more useful to critically investigate the plausibility of the statistical assumptions underlying analyses than to limit themselves to the set of analyses that Stevens believed to be admissible with data of a given level of measurement.