Experimental neuroscience typically uses “p-valued” statistical testing procedures (null hypothesis significance testing; NHST) in evaluating its results. The rote, often misguided, application of NHST (Gigerenzer, 2008) has led to errors and “questionable research practices.” Although the problems could be avoided with better statistics training (Lakens, 2021), there have been calls to abandon NHST altogether. One suggestion is to replace NHST with “estimation statistics” (Cumming and Calin-Jageman, 2017; Calin-Jageman and Cumming, 2019). Estimation statistics emphasizes the uncertainty inherent in scientific investigations and uses metrics, e.g., confidence intervals (CIs), that draw attention to uncertainty. Besides procedural steps and methods, the Estimation Approach prefers expressing “quantitative,” rather than “qualitative” conclusions and making generalizations, rather than testing scientific hypotheses. The Estimation Approach embodies a philosophy of science—its ultimate goals, experimental mindset, and specific aims—that diverges unhelpfully from what laboratory-based neuroscience needs. The Estimation Approach meshes naturally with, e.g., clinical neuroscience, drug de
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