OPTIMAL TESTS OF SIGNIFICANCE is a research paper published in Australian Journal of Statistics (1979). On theSindex it has a DataRank of 0.452. It has been cited 4 times, with 4 citing works in its 1-hop citation network.
SummaryTo perform a test of significance of a null hypothesis, a test statistic is chosen which is expected to be small if the hypothesis is false. Then the significance level of the test for an observed sample is the probability that the test statistic, under the assumptions of the hypothesis, is as small, or smaller than, its observed value. A “good” test statistic is taken to be one which is stochastically small when the null hypothesis is false. Optimal test statistics are defined using this criterion and the relationship of these methods to the Neyman‐Pearson theory of hypothesis testing is considered.
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
0.211
From 2 citing papers with measurable signal
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 53% comes from its base citations and 47% from the citation network (2 citing papers 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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