TY - GEN

T1 - Two sample T-tests for IR evaluation

T2 - 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016

AU - Sakai, Tetsuya

N1 - Publisher Copyright:
© 2016 ACM.

PY - 2016/7/7

Y1 - 2016/7/7

N2 - There are two well-known versions of the t-test for comparing means from unpaired data: Student's t-test and Welch's t-test. While Welch's t-test does not assume homoscedasticity (i.e., equal variances), it involves approximations. A classical textbook recommendation would be to use Student's t-test if either the two sample sizes are similar or the two sample variances are similar, and to use Welch's t-test only when both of the above conditions are violated. However, a more recent recommendation seems to be to use Welch's t-test unconditionally. Using past data from both TREC and NTCIR, the present study demonstrates that the latter advice should not be followed blindly in the context of IR system evaluation. More specifically, our results suggest that if the sample sizes differ substantially and if the larger sample has a substantially larger variance, Welch's t-test may not be reliable.

AB - There are two well-known versions of the t-test for comparing means from unpaired data: Student's t-test and Welch's t-test. While Welch's t-test does not assume homoscedasticity (i.e., equal variances), it involves approximations. A classical textbook recommendation would be to use Student's t-test if either the two sample sizes are similar or the two sample variances are similar, and to use Welch's t-test only when both of the above conditions are violated. However, a more recent recommendation seems to be to use Welch's t-test unconditionally. Using past data from both TREC and NTCIR, the present study demonstrates that the latter advice should not be followed blindly in the context of IR system evaluation. More specifically, our results suggest that if the sample sizes differ substantially and if the larger sample has a substantially larger variance, Welch's t-test may not be reliable.

KW - Statistical significance

KW - Test collections

KW - Topics

KW - Variances

UR - http://www.scopus.com/inward/record.url?scp=84980398049&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84980398049&partnerID=8YFLogxK

U2 - 10.1145/2911451.2914684

DO - 10.1145/2911451.2914684

M3 - Conference contribution

AN - SCOPUS:84980398049

T3 - SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

SP - 1045

EP - 1048

BT - SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - Association for Computing Machinery, Inc

Y2 - 17 July 2016 through 21 July 2016

ER -