![]() The p-value has been subjected to much speculation, analysis, and criticism. (Null Hypothesis) Significance Testing (ST) is the most prominent of these methods. This probability is commonly known as 'the' p-value. Many statistical methods yield the probability of the observed data - or data more extreme - under the assumption that a particular hypothesis is true. Like knives or axes, NHST is not inherently evil only misuse and misinterpretation of its outcomes needs to be eradicated. Those alternatives also fail to cover a broad area of inference not involving distributional parameters, where NHST procedures remain the only (and suitable) option. None of them offers a solution to the problems that NHST is purported to have, all of them are susceptible to misuse and misinterpretation, and some bring around their own problems (e.g., Bayes factors have a one-to-one correspondence with p values, but they are entirely deprived of an inferential framework). Three of those alternatives are discussed here (estimation via confidence intervals and effect sizes, quantification of evidence via Bayes factors, and mere reporting of descriptive statistics). Ironically, alternatives to NHST are identical in these respects, something that has been overlooked in their presentation. NHST has been criticized for its misuse and the misconstruction of its outcomes, also stressing its inability to meet expectations that it was never designed to fulfil. Hence, raising conclusions from the outcomes of statistical analyses is subject to limitations. Inference is an inverse problem and application of statistical methods cannot reveal whether effects exist or whether they are empirically meaningful. This article addresses this debate from the perspective of scientific inquiry and inference. ![]() Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed.
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