  Statistical significance testing centered on p-values is commonly used to
compare NLP system performance, but p-values alone are insufficient because
statistical significance differs from practical significance. The latter can be
measured by estimating effect size. In this paper, we propose a three-stage
procedure for comparing NLP system performance and provide a toolkit,
NLPStatTest, that automates the process. Users can upload NLP system evaluation
scores and the toolkit will analyze these scores, run appropriate significance
tests, estimate effect size, and conduct power analysis to estimate Type II
error. The toolkit provides a convenient and systematic way to compare NLP
system performance that goes beyond statistical significance testing
