evaluating evaluation metrics based on the bootstrap
This paper describes how the Bootstrap approach to statistics can be applied to the evaluation of IR effectiveness metrics. First, we argue that Bootstrap Hypothesis Tests deserve more attention from the IR community, as they are based on fewer assumptions than traditional statistical significance tests. We then describe straightforward methods for comparing the sensitivity of IR metrics based on Bootstrap Hypothesis Tests. Unlike the heuristics-based "swap" method proposed by Voorhees and Buckley, our method estimates the performance difference required to achieve a given significance level directly from Bootstrap Hypothesis Test results. In addition, we describe a simple way of examining the accuracy of rank correlation between two metrics based on the Bootstrap Estimate of Standard Error. We demonstrate the usefulness of our methods using test collections and runs from the NTCIR CLIR track for comparing seven IR metrics, including those that can handle graded relevance and those based on the Geometric Mean.
evaluation metrication bootstrap paper statistics effect test attention communities method sensitive estimates performance accuracy ranking correlation standardization errors demonstrate use test collection tracking graded relevance relevance mean heuristics information retrieval