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.
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.
Thorburn
Daniel
D
Department of Statistics, Stockholm University, SE-10691 Stockholm, Sweden. Daniel.Thorburn@stat.su.se
D001499:Bayes Theorem; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D012106:Research / Q000706:statistics & numerical data*; D012107:Research Design