The null hypothesis is always rejected with statistical tricks: Why do you need it? is a research paper published in Revista Interamericana de Psicología/Interamerican Journal of Psychology (2019). On theSindex it has a DataRank of 0.327. It has been cited 4 times, with 3 citing works in its 1-hop citation network.
Ferguson (2015) observed that the proportion of studies supporting the experimental hypothesis and rejecting the null hypothesis is very high. This paper argues that the reason for this scenario is that researchers in the behavioral sciences have learned that the null hypothesis can always be rejected if one knows the statistical tricks to reject it (e.g., the probability of rejecting the null hypothesis increases with p = 0.05 compare to p = 0.01). Examples of the advancement of science without the need to formulate the null hypothesis are also discussed, as well as alternatives to null hypothesis significance testing-NHST (e.g., effect sizes), and the importance to distinguish the statistical significance from the practical significance of results.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.241
From this paper's citation signal
Citation Network Contribution
0.0860
From 1 citing papers with measurable signal
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 74% comes from its base citations and 26% from the citation network (1 citing paper contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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