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Significance testing, interval estimation or Bayesian inference: Comments to “Extracting a maximum of useful information from statistical research data” by S. Sohlberg and G. Andersson
Author(s) -
THORBURN DANIEL
Publication year - 2005
Publication title -
scandinavian journal of psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 72
eISSN - 1467-9450
pISSN - 0036-5564
DOI - 10.1111/j.1467-9450.2005.00437.x
Subject(s) - inference , bayesian inference , statistical inference , frequentist inference , statistical hypothesis testing , bayesian probability , psychology , interval estimation , null hypothesis , bayesian statistics , frequentist probability , fiducial inference , significance testing , statistical significance , econometrics , computer science , statistics , artificial intelligence , confidence interval , mathematics
Statistical inference plays an important part in the formation of scientific knowledge in psychology. Starting from a paper by Sohlberg and Andersson (2005; Scandinavian Journal of Psychology , 46 , 69–77) these issues are discussed. It is argued that interval estimates are easy to understand and that they are more suitable than significance testing for most problems. Bayesian inference is a coherent description of the information building process. With some examples it is shown that null hypothesis significance testing is full of contradictions. Finally, some other important issues like convenience sampling and model selection are shortly mentioned.