Good practice in testing for an association in contingency tables

Speaker: Markus Neuhäuser

Affiliation: Rhein Ahr Campus

Time: Monday 24/01/2011 from 14:00 to 15:00

Venue: Access Grid UWS. Presented from Parramatta (EB.1.32), accessible from Campbelltown (26.1.50) and Penrith (Y239).

Abstract: The testing for an association between two categorical variables using count data is commonplace in statistical practice. Here, we present evidence that influential biostatistical textbooks give contradictory and incomplete advice on good practice in the analysis of such contingency table data. We survey the statistical literature and offer guidance on such analyses. Specifically, we call for greater use of exact testing rather than tests which use an asymptotic chi-squared distribution. That is, we suggest that researchers take a conservative approach and only perform asymptotic testing where there is little doubt that it is appropriate. We recommend a specific criterion for such decision-making. Where asymptotic testing is appropriate, we recommend chi-squared over the G-test and recommend against the implementation of Yates (or any other) correction. We also provide advice on the effective use of exact testing for associations in contingency tables. Lastly, we highlight issues that need to be considered when using the commonly recommended Fisher's exact test. Reference: Ruxton GD & Neuhäuser M (2010): Good practice in testing for an association in contingency tables. Behav Ecol Sociobiol 64, 1505-1513)

Biography: Markus is a Professor at the Department of Mathematics and Technology, Rhein Ahr Campus