The Effect Of Topic Set Size On Retrieval Experiment Error

Abstract

Retrieval mechanisms are frequently compared by computing the respective average scores for some effectiveness metric across a common set of information needs or topics, with researchers concluding one method is superior based on those averages. Since comparative retrieval system behavior is known to be highly variable across topics, good experimental design requires that a "sufficient" number of topics be used in the test. This paper uses TREC results to empirically derive error rates based on the number of topics used in a test and the observed difference in the average scores. The error rates quantify the likelihood that a different set of topics of the same size would lead to a different conclusion. We directly compute error rates for topic sets up to size 25, and extrapolate those rates for larger topic set sizes. The error rates found are larger than anticipated, indicating researchers need to take care when concluding one method is better than another, especially if few topics are used.