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    Conscious and Correct Use of Biostatistical Methods in Medical Researches: From Planning to Reporting the Results - Part I
    (2022) Karabulut, Erdem
    The principles and methods of biostatistics serve as a guide for healthcare practitioners in both their daily and scientific work. Biostatistics principles and methods should be considered at every stage. A scientific research requires a multidisciplinary teamwork and each team must include a biostatistics expert at the planning stage of the study. During the planning phase, a biostatistics expert can answer all questions about the design, conduct, data processing, data analysis, presentation of the results, and publication of the results. Reporting guidelines for almost all research designs were developed to improve the quality and transparency of research reports. When the research results are presented in a structured and standardized way using these guidelines, it will allow the readers to interpret the findings more easily and accurately. It is now easier for researchers to gain access to a variety of software or web applications that allow them to perform basic and advanced statistical analyses. However, erroneous/misleading results are produced when these software are used unconsciously or incorrectly by those with insufficient statistical knowledge. Health professionals should also have sufficient statistical knowledge to identify errors in articles related to their field. Therefore, they are expected to have knowledge of research designs and basic statistical concepts. Knowing the basic concepts such as population, sample, sampling methods, random assignment of volunteers to groups, parameters, statistics, data types, etc. will help the readers while evaluating the material and method sections of the articles.
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    Conscious and Correct Use of Biostatistical Methods in Medical Researches: From Planning to Reporting the Results - Part II
    (Düzce Üniversitesi, 2024) Sungur, Mehmet Ali; Karabulut, Erdem
    In this part of the review, statistical tests utilized to examine hypotheses regarding population parameters on a representative sample, which forms the fundamentals of inferential statistics are discussed. The selection of an appropriate statistical test by verifying its assumptions and interpreting the results objectively is crucial for obtaining accurate conclusions. Understanding the terms related to type I and type II errors, p-value, power of the study, effect size, and confidence interval will contribute to the correct interpretation of both the results obtained from statistical tests in scientific research and the findings of articles read from the literature. In addition to univariate tests, the three most commonly employed multiple regression models are also addressed to control for the effect of potential confounding factors and other independent variables utilized in the study. Statistical computing has become much more accessible in recent times, with researchers having access to freeware packages or web applications to perform basic and advanced statistical analyses. Researchers frequently focus on the calculation of statistical tests used in data analysis, whereas, understanding the rationale behind statistical methods should be the primary goal. Therefore, this review emphasizes the logic of selecting appropriate statistical methods and interpreting the results rather than mathematical calculations. It is essential to recognize that biostatistical principles should be considered not only in the data analysis phase but also in all phases of research, from planning to report writing. It should be note that, no statistical analysis method can correct erroneous data obtained from a poorly designed study.