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Year : 2017  |  Volume : 3  |  Issue : 2  |  Page : 268-270

Type I, II, and III statistical errors: A brief overview

1 Research Institute, St. Luke's University Health Network, Bethlehem, United States of America
2 Research Institute, St. Luke's University Health Network, Bethlehem; Temple University School of Medicine, Philadelphia, PA, United States of America

Correspondence Address:
Dr. Jill Stoltzfus
St. Luke's University Health Network, 801 Ostrum Street, Bethlehem, PA 18015
United States of America
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/IJAM.IJAM_92_17

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As a key component of scientific research, hypothesis testing incorporates a null hypothesis (H0) of no difference in a larger population and an alternative hypothesis (H1or HA) that becomes true when the null hypothesis is shown to be false. Two potential types of statistical error are Type I error (α, or level of significance), when one falsely rejects a null hypothesis that is true, and Type II error (β), when one fails to reject a null hypothesis that is false. To reduce Type I error, one should decrease the pre-determined level of statistical significance. To decrease Type II error, one should increase the sample size in order to detect an effect size of interest with adequate statistical power. Reducing Type I error tends to increase Type II error, and vice versa. Type III error, although rare, occurs when one correctly rejects the null hypothesis of no difference, but does so for the wrong reason. The following core competencies are addressed in this article: Practice-based learning and improvement, Medical knowledge.

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