Why we need to report more than 'Data were Analyzed by t-tests or ANOVA'

Author:

Weissgerber Tracey L12ORCID,Garcia-Valencia Oscar1ORCID,Garovic Vesna D1ORCID,Milic Natasa M13,Winham Stacey J4ORCID

Affiliation:

1. Division of Nephrology and Hypertension, Mayo Clinic, Rochester, United States

2. QUEST, Charité - Universitätsmedizin Berlin, Berlin Institutes of Health, Berlin, Germany

3. Department of Medical Statistics & Informatics, Medical Faculty, University of Belgrade, Belgrade, Serbia

4. Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, United States

Abstract

Transparent reporting is essential for the critical evaluation of studies. However, the reporting of statistical methods for studies in the biomedical sciences is often limited. This systematic review examines the quality of reporting for two statistical tests, t-tests and ANOVA, for papers published in a selection of physiology journals in June 2017. Of the 328 original research articles examined, 277 (84.5%) included an ANOVA or t-test or both. However, papers in our sample were routinely missing essential information about both types of tests: 213 papers (95% of the papers that used ANOVA) did not contain the information needed to determine what type of ANOVA was performed, and 26.7% of papers did not specify what post-hoc test was performed. Most papers also omitted the information needed to verify ANOVA results. Essential information about t-tests was also missing in many papers. We conclude by discussing measures that could be taken to improve the quality of reporting.

Funder

American Heart Association

National Center for Advancing Translational Sciences

Mayo Clinic

National Cancer Institute

Walter and Evelyn Simmers Career Development Award for Ovarian Cancer Research

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference35 articles.

1. The new statistics: why and how;Cumming;Psychological Science,2014

2. Poor statistical reporting, inadequate data presentation and spin persist despite editorial advice;Diong;PLoS One,2018

3. Thinking outside the box: developing dynamic data visualizations for psychology with shiny;Ellis;Frontiers in Psychology,2015

4. Author Guidelines (The EMBO Journal);EMBO Press,2017

5. statcheck: Extract statistics from articles and recompute p values;Eskamp,2016

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3