Aug 2026· Humanities and Social Sciences Communications· Vol 13· 0 citations· 35 references
Abstract
The p-value has long served as the standard of scientific significance, but its widespread misuse and misunderstanding continue to distort how we interpret data and publish findings. In this Comment, we take a critical look at the scientific overreliance on p-values in quantitative research. Through two illustrative case studies, we showcase how statistical significance can both deceive and obscure, depending on how models are specified and results interpreted. We highlight common misconceptions and argue for a shift in both statistical education and publication norms. This is not a call to abandon the p-value, but a call to reframe it as one small piece in an extended conversation about evidence, uncertainty, and meaning.
In this paper, we critically examine the essay by Shiffrin, Stigler, and Keil (2026) on scientific understanding. We argue that, despite its uncontroversial moral message, the paper largely fails to articulate a compelling thesis. The central notion of an “illusion of understanding” is left insufficiently specified and...
David Kellen, M. S. Spektor· Computational Brain & Behavi...· 0 citations
Oreskes and Conway’s (2010) paradigmatic cases for agnotology show how misplaced doubt can be deliberately produced in the minds of one’s audience, even when everything one asserts is strictly true. This phenomenon is well captured by a Gricean framework that places stress on the ability to convey false implicatures vi...
T. Lewens· European Journal for Philoso...· 0 citations
Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment settings studied in most sycophancy research. We test whether editorial framing in the prompt, ranging from a neutral request to an explicit instruction to search exhau...
Statistical hypothesis tests and
p
values are poorly understood by many students of statistics. Poor application and misinterpretation of statistical inference procedures are common in published research. Over‐reliance on the “
p
< 0.05” threshold to claim “statistical significance” often masks weaknesses in th...
The p-value is one of the most widely used statistical measures in quantitative educational research, particularly within null hypothesis significance testing. Despite its widespread use, p-values are frequently misunderstood and incorrectly interpreted by researchers and users of research findings. This conceptual pap...
Aliyu Muhammad, Abbas Muhammad Labbo, Hauwa Muhammad Aliyu et al.· Aminu Kano Academic Scholars...· 0 citations
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