Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Meta-Anal. Jun 18, 2025; 13(2): 104382
Published online Jun 18, 2025. doi: 10.13105/wjma.v13.i2.104382
Importance of data collection and subgroup analyses in research methodology
Sunny Chi Lik Au
Sunny Chi Lik Au, Department of Ophthalmology, Tung Wah Eastern Hospital, Hong Kong 999077, China
Author contributions: Au SCL designed the research, performed the research, acquired the data, analyzed the data, drafted the manuscript, and revised the manuscript.
Conflict-of-interest statement: All the authors have disclosed no conflicts of interest. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Sunny Chi Lik Au, Chief Physician, Research Fellow, Department of Ophthalmology, Tung Wah Eastern Hospital, 9/F, MO Office, Lo Ka Chow Memorial Ophthalmic Centre, No. 19 Eastern Hospital Road, Causeway Bay, Hong Kong 999077, China. kilihcua@gmail.com
Received: December 19, 2024
Revised: March 29, 2025
Accepted: April 11, 2025
Published online: June 18, 2025
Processing time: 179 Days and 20.1 Hours
Core Tip

Core Tip: A variety of methods exist to assess the normality of continuous data. Among these tests, the Shapiro–Wilk test, Kolmogorov–Smirnov test, skewness, kurtosis, histogram, box plot, P–P plot, Q–Q plot, and mean with standard deviation are commonly employed. Of these, the widely recognized Shapiro–Wilk test is suitable for small sample sizes (< 50 samples), although it can also accommodate larger sample sizes. Conversely, the Kolmogorov–Smirnov test finds utility when dealing with n ≥ 50, making it another prominent technique for evaluating data normality.