Estimation statistics should replace significance testing
By Adam Claridge‐Chang, P. Assam. 2016
315 citations recorded by the source corpus.
studies can be combined for meta-analysis, enabling researchers to home in on the true effect. Although we do not encourage the use of power analysis, Lazzeroni et al.’s figure supports our own illustration of the variability in P. As both our models7 and Lazzeroni et al.’s models demonstrate, unless the results of an experiment show a very marked pattern in the data, the reported P value will be accompanied by limits so broad as to render P uninterpretable. Put simply, P is untrustworthy unless the statistical power is very high (above 90%), which offsets advantages of P such as its simplicity. As researchers better appreciate the typically artificial nature of the null hypothesis3 and the limited capacity of P to support hypothesis testing, we believe that P will become much less highly valued.
Subfields: Computational Theory and Mathematics, Small Animals
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In Computational Theory and Mathematics
Citations in the corpus
- EXTL3 mutations cause skeletal dysplasia, immune deficiency, and developmental delay (2017, background): “…Phred score (17.64 for the p.R339W mutation and 15.4 for the p.P461L mutation) is significantly higher than the mutation significance cut off (Itan et al., 2016), which for the EXTL3 gene is 8.931; and (d) defects of skeletal development had been previously reported in the boxtm70g…”
- The statistical significance filter leads to overoptimistic expectations of replicability (2018, background): “Many researchers have pointed out that we should aim for higherprecision estimates and focus on estimation rather than only focusing on statistical significance (e.g., Claridge-Chang & Assam, 2016; Greenland et al., 2016).”