J Korean Assoc Oral Maxillofac Surg 2018; 44(1): 25~28
Survey of the use of statistical methods in Journal of the Korean Association of Oral and Maxillofacial Surgeons
Yong-Geun Choi
Epidemiology & Biostatistics, Korea University Graduate School of Clinical Dentistry, Seoul, Korea
Yong-Geun Choi
Epidemiology & Biostatistics, Korea University Graduate School of Clinical Dentistry, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
TEL: +82-2-3394-7555 FAX: +82-2-3394-6875
E-mail: ebdent@snu.ac.kr
ORCID: http://orcid.org/0000-0003-1430-8228
Received August 25, 2017; Revised November 1, 2017; Accepted November 20, 2017.; Published online February 28, 2018.
© Korean Association of Oral and Maxillofacial Surgeons. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
Objectives: This study aimed to describe recent patterns in the types of statistical test used in original articles that were published in Journal of the Korean Association of Oral and Maxillofacial Surgeons.
Materials and Methods: Thirty-six original articles published in the Journal in 2015 and 2016 were ascertained. The type of statistical test was identified by one researcher. Descriptive statistics, such as frequency, rank, and proportion, were calculated. Graphical statistics, such as a histogram, were constructed to reveal the overall utilization pattern of statistical test types.
Results: Twenty-two types of statistical test were used. Statistical test type was not reported in four original articles and classified as unclear in 5%. The four most frequently used statistical tests constituted 47% of the total tests and these were the chi-square test, Student’s t-test, Fisher’s exact test, and Mann-Whitney test in descending order. Regression models, such as the Cox proportional hazard model and multiple logistic regression to adjust for potential confounding variables, were used in only 6% of the studies. Normality tests, including the Kolmogorov-Smirnov test, Levene test, Shapiro-Wilk test, and Scheffé’s test, were used diversely but in only 10% of the studies.
Conclusion: A total of 22 statistical tests were identified, with four tests occupying almost half of the results. Adoption of a nonparametric test is recommended when the status of normality is vague. Adjustment for confounding variables should be pursued using a multiple regression model when the number of potential confounding variables is numerous.
Keywords: Statistics, Normal distribution, Confounding factors


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