Data for Example II.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

唯有对所使用统计方法的底层假设进行验证，数据分析方能准确可靠。若这些假设遭到违反，将会改变分析的结果与结论。本研究开发了智能数据分析工具V2（Smart Data Analysis V2，简称SDA-V2），一款兼具交互性与易用性的Web应用程序，旨在帮助统计知识有限的用户开展数据分析，用户可通过https://jularatchumnaul.shinyapps.io/SDA-V2/免费访问该工具。SDA-V2可自动对数据进行探索与可视化，检验与参数检验相关的底层假设，并为给定数据集选择合适的统计方法。此外，该工具还可评估研究工具的质量，并确定有意义研究所需的最小样本量。尽管SDA-V2是简化统计分析的实用工具，但它无法替代对统计原理的基础理解。我们鼓励研究者将自身专业知识与该软件的功能相结合，以获得最为准确可信的分析结果。