ABSTRACT
CRITICAL VALUES FOR THE TWO INDEPENDENT SAMPLES WINSORIZED T TEST
by
PIPER A. FARRELL-SINGLETON
AUGUST 2010
Advisor: Dr. Shlomo Sawilowsky
Major: (Education, Evaluation and Research)
Degree: Doctor of Philosophy
Through Monte Carlo Simulation, this study explores the approximate behavior of the two sample winsorized t test. Samples are drawn from the normal population and symetrically winsorized up to 20%. The two independent samples winsorized t test is then calculated on each sample using Monte Carlo methods using 1,000,000 iterations. The t values are then sorted from low to high and the critical values for both one and two tailed tests identified at the 95th, 97.5th, 99th and 99.5th percentiles. A table of critical values is then created, which represents the approximate distribution of the winsorized t statistic.
","abstract_html":"<p>ABSTRACT</p> <p>CRITICAL VALUES FOR THE TWO INDEPENDENT SAMPLES WINSORIZED T TEST</p> <p>by</p> <p>PIPER A. FARRELL-SINGLETON</p> <p>AUGUST 2010</p> <p>Advisor: Dr. Shlomo Sawilowsky</p> <p>Major: (Education, Evaluation and Research)</p> <p>Degree: Doctor of Philosophy</p> <p>Through Monte Carlo Simulation, this study explores the approximate behavior of the two sample winsorized t test. Samples are drawn from the normal population and symetrically winsorized up to 20%. The two independent samples winsorized t test is then calculated on each sample using Monte Carlo methods using 1,000,000 iterations. The t values are then sorted from low to high and the critical values for both one and two tailed tests identified at the 95th, 97.5th, 99th and 99.5th percentiles. A table of critical values is then created, which represents the approximate distribution of the winsorized t statistic.</p>","abstract_has_math":false,"creators":["Farrell-Singleton, Piper Alycee"],"institution":null,"degree_name":"Ph.D.","degree_level":"Open Access Dissertation","degree_discipline":"Education Evaluation and Research","degree_department":null,"school":null,"contributors":["Shlomo Sawilowsky"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T05:58:35Z","subjects":["winsorization","winsorize","winsorized t test","Statistics and Probability"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wayne.edu/oa_dissertations/86","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shlomo Sawilowsky"]},{"key":"dc:creator","label":"Author","values":["Farrell-Singleton, Piper Alycee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2010-09-30T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Education Evaluation and Research"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["winsorization","winsorize","winsorized t test","Statistics and Probability"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wayne.edu/oa_dissertations/86"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["ABSTRACT
CRITICAL VALUES FOR THE TWO INDEPENDENT SAMPLES WINSORIZED T TEST
by
PIPER A. FARRELL-SINGLETON
AUGUST 2010
Advisor: Dr. Shlomo Sawilowsky
Major: (Education, Evaluation and Research)
Degree: Doctor of Philosophy
Through Monte Carlo Simulation, this study explores the approximate behavior of the two sample winsorized t test. Samples are drawn from the normal population and symetrically winsorized up to 20%. The two independent samples winsorized t test is then calculated on each sample using Monte Carlo methods using 1,000,000 iterations. The t values are then sorted from low to high and the critical values for both one and two tailed tests identified at the 95th, 97.5th, 99th and 99.5th percentiles. A table of critical values is then created, which represents the approximate distribution of the winsorized t statistic.
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CRITICAL VALUES FOR THE TWO INDEPENDENT SAMPLES WINSORIZED T TEST
by
PIPER A. FARRELL-SINGLETON
AUGUST 2010
Advisor: Dr. Shlomo Sawilowsky
Major: (Education, Evaluation and Research)
Degree: Doctor of Philosophy
Through Monte Carlo Simulation, this study explores the approximate behavior of the two sample winsorized t test. Samples are drawn from the normal population and symetrically winsorized up to 20%. The two independent samples winsorized t test is then calculated on each sample using Monte Carlo methods using 1,000,000 iterations. The t values are then sorted from low to high and the critical values for both one and two tailed tests identified at the 95th, 97.5th, 99th and 99.5th percentiles. A table of critical values is then created, which represents the approximate distribution of the winsorized t statistic.
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