Results 121 to 130 of about 6,626,690 (175)

Bayesian Thinking in the Intensive Care Unit: From Statistical Theory to Clinical Practice. [PDF]

open access: yesIndian J Crit Care Med
Schultz MJ   +4 more
europepmc   +1 more source

Why reporting requires more than findings. [PDF]

open access: yesExp Physiol
Christensen R   +2 more
europepmc   +1 more source

In support of null hypothesis significance testing [PDF]

open access: yesProceedings of the Royal Society B: Biological Sciences, 2004
Many criticisms have been levelled at null hypothesis significance testing (NHST). It is argued here that although there is reason to doubt that data subjected only to NHST have been subjected to sufficient analysis, the search for clear answers to well-formulated questions derived from substantive hypotheses is well served by NHST.
exaly   +3 more sources

Power Analysis for Null Hypothesis Significance Testing

Clinical Spine Surgery, 2020
Before conducting a scientific study, a power analysis is performed to determine the sample size required to test an effect within allowable probabilities of Type I error (α) or Type II error (β). The power of a study is related to Type II error by 1−β. Most scientific studies set α=0.05 and power=0.80 as minimums.
Kristen J, Nicholson   +4 more
openaire   +2 more sources

A tutorial on a practical Bayesian alternative to null-hypothesis significance testing [PDF]

open access: yesBehavior Research Methods, 2011
Null-hypothesis significance testing remains the standard inferential tool in cognitive science despite its serious disadvantages. Primary among these is the fact that the resulting probability value does not tell the researcher what he or she usually wants to know: How probable is a hypothesis, given the obtained data?
Michael Masson
exaly   +3 more sources

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