, Volume 35, Issue 2, pp 237–243 | Cite as

Extensions of the significance test for one-parameter signal detection hypotheses

  • Leonard A. Marascuilo


The basic models of signal detection theory involve the parametric measure,d′, generally interpreted as a detectability index. Given two observers, one might wish to know whether their detectability indices are equal or unequal. Gourevitch and Galanter (1967) proposed a large sample statistical test that could be used to test the hypothesis of equald′ values. In this paper, their large two sample test is extended to aK-sample detection test. If the null hypothesisd1′=d2′=...=d K ′ is rejected, one can employ the post hoc confidence interval procedure described in this paper to locate possible statistically significant sources of variance and differences. In addition, it is shown how one can use the Gourevitch and Galanter statistics to testd′=0 for a single individual.


Confidence Interval Sample Test Public Policy Significance Test Statistical Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Gourevitch, V. & Galanter, E. A significance test for one parameter isosensitivity functions.Psychometrika, 1967,32, 25–33.Google Scholar
  2. Marascuilo, L. A. Large sample multiple comparisons,Psychological Bulletin, 1966,65, 280–290.Google Scholar
  3. Marascuilo, L. A. & McSweeney, M. Multiple contrast methods for analytical surveys.Proceedings of the Social Statistics Section of the American Statistical Association, 1967, 336–341.Google Scholar
  4. Scheffé, H.The Analysis of Variance. New York: John Wiley and Sons, 1959.Google Scholar
  5. Swets, J. A. Is there a sensory threshold?.Science, 21 July 1961,134, 168–176.Google Scholar

Copyright information

© Psychometric Society 1970

Authors and Affiliations

  • Leonard A. Marascuilo
    • 1
  1. 1.University of CaliforniaBerkeley

Personalised recommendations