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Statistical Significance Testing in Information Retrieval : An Empirical Analysis of Type I, Type II and Type III Errors. / Urbano, Julián; De Lima, Harlley; Hanjalic, Alan.

SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York, USA : ACM DL, 2019. p. 505-514.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Urbano, J, De Lima, H & Hanjalic, A 2019, Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors. in SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM DL, New York, USA, pp. 505-514, SIGIR 2019: the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval , Paris, France, 21/07/19. https://doi.org/10.1145/3331184.3331259

APA

Urbano, J., De Lima, H., & Hanjalic, A. (2019). Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors. In SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 505-514). New York, USA: ACM DL. https://doi.org/10.1145/3331184.3331259

Vancouver

Urbano J, De Lima H, Hanjalic A. Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors. In SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York, USA: ACM DL. 2019. p. 505-514 https://doi.org/10.1145/3331184.3331259

Author

Urbano, Julián ; De Lima, Harlley ; Hanjalic, Alan. / Statistical Significance Testing in Information Retrieval : An Empirical Analysis of Type I, Type II and Type III Errors. SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York, USA : ACM DL, 2019. pp. 505-514

BibTeX

@inproceedings{aff400caecfe40b0aed679ef2ee56afe,
title = "Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors",
abstract = "Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random noise because of the selection of topics. According to recent surveys on SIGIR, CIKM, ECIR and TOIS papers, the t-test is the most popular choice among IR researchers. However, previous work has suggested computer intensive tests like the bootstrap or the permutation test, based mainly on theoretical arguments. On empirical grounds, others have suggested non-parametric alternatives such as the Wilcoxon test. Indeed, the question of which tests we should use has accompanied IR and related fields for decades now. Previous theoretical studies on this matter were limited in that we know that test assumptions are not met in IR experiments, and empirical studies were limited in that we do not have the necessary control over the null hypotheses to compute actual Type I and Type II error rates under realistic conditions. Therefore, not only is it unclear which test to use, but also how much trust we should put in them. In contrast to past studies, in this paper we employ a recent simulation methodology from TREC data to go around these limitations. Our study comprises over 500 million p-values computed for a range of tests, systems, effectiveness measures, topic set sizes and effect sizes, and for both the 2-tail and 1-tail cases. Having such a large supply of IR evaluation data with full knowledge of the null hypotheses, we are finally in a position to evaluate how well statistical significance tests really behave with IR data, and make sound recommendations for practitioners.",
keywords = "Statistical significance,, Student’s t-test, Wilcoxon test, Sign test, Bootstrap, Permutation, Simulation, Type I and Type II errors",
author = "Juli{\'a}n Urbano and {De Lima}, Harlley and Alan Hanjalic",
year = "2019",
doi = "10.1145/3331184.3331259",
language = "English",
isbn = "978-1-4503-6172-9",
pages = "505--514",
booktitle = "SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "ACM DL",

}

RIS

TY - GEN

T1 - Statistical Significance Testing in Information Retrieval

T2 - An Empirical Analysis of Type I, Type II and Type III Errors

AU - Urbano, Julián

AU - De Lima, Harlley

AU - Hanjalic, Alan

PY - 2019

Y1 - 2019

N2 - Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random noise because of the selection of topics. According to recent surveys on SIGIR, CIKM, ECIR and TOIS papers, the t-test is the most popular choice among IR researchers. However, previous work has suggested computer intensive tests like the bootstrap or the permutation test, based mainly on theoretical arguments. On empirical grounds, others have suggested non-parametric alternatives such as the Wilcoxon test. Indeed, the question of which tests we should use has accompanied IR and related fields for decades now. Previous theoretical studies on this matter were limited in that we know that test assumptions are not met in IR experiments, and empirical studies were limited in that we do not have the necessary control over the null hypotheses to compute actual Type I and Type II error rates under realistic conditions. Therefore, not only is it unclear which test to use, but also how much trust we should put in them. In contrast to past studies, in this paper we employ a recent simulation methodology from TREC data to go around these limitations. Our study comprises over 500 million p-values computed for a range of tests, systems, effectiveness measures, topic set sizes and effect sizes, and for both the 2-tail and 1-tail cases. Having such a large supply of IR evaluation data with full knowledge of the null hypotheses, we are finally in a position to evaluate how well statistical significance tests really behave with IR data, and make sound recommendations for practitioners.

AB - Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random noise because of the selection of topics. According to recent surveys on SIGIR, CIKM, ECIR and TOIS papers, the t-test is the most popular choice among IR researchers. However, previous work has suggested computer intensive tests like the bootstrap or the permutation test, based mainly on theoretical arguments. On empirical grounds, others have suggested non-parametric alternatives such as the Wilcoxon test. Indeed, the question of which tests we should use has accompanied IR and related fields for decades now. Previous theoretical studies on this matter were limited in that we know that test assumptions are not met in IR experiments, and empirical studies were limited in that we do not have the necessary control over the null hypotheses to compute actual Type I and Type II error rates under realistic conditions. Therefore, not only is it unclear which test to use, but also how much trust we should put in them. In contrast to past studies, in this paper we employ a recent simulation methodology from TREC data to go around these limitations. Our study comprises over 500 million p-values computed for a range of tests, systems, effectiveness measures, topic set sizes and effect sizes, and for both the 2-tail and 1-tail cases. Having such a large supply of IR evaluation data with full knowledge of the null hypotheses, we are finally in a position to evaluate how well statistical significance tests really behave with IR data, and make sound recommendations for practitioners.

KW - Statistical significance,

KW - Student’s t-test

KW - Wilcoxon test

KW - Sign test

KW - Bootstrap

KW - Permutation

KW - Simulation

KW - Type I and Type II errors

UR - https://github.com/julian-urbano/sigir2019-statistical

U2 - 10.1145/3331184.3331259

DO - 10.1145/3331184.3331259

M3 - Conference contribution

SN - 978-1-4503-6172-9

SP - 505

EP - 514

BT - SIGIR'19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - ACM DL

CY - New York, USA

ER -

ID: 55635612