Using confidence intervals for graphically based data interpretation.
As a potential alternative to standard null hypothesis significance testing, we describe methods for graphical presentation of data--particularly condition means and their corresponding confidence intervals--for a wide range of factorial designs used in experimental psychology. We describe and illustrate confidence intervals specifically appropriate for between-subject versus within-subject factors. For designs involving more than two levels of a factor, we describe the use of contrasts for graphical illustration of theoretically meaningful components of main effects and interactions. These graphical techniques lend themselves to a natural and straightforward assessment of statistical power.
Masson
Michael E J
ME
Department of Psychology, University of Victoria, British Columbia. mmasson@uvic.ca
D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*