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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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Avoiding common problems with statistical analysis of biological experiments using a simple nested data simulatorстатья Исследовательская статья
- Авторы: Alexandrova Veronika, Anisimov Mikhail, Eltsov Igor, Kilina Anastasia, Lopanskaia Iuliia, Makarova Liubov, Vovchenko Maxim, Gudimchuk Nikita
- Журнал: Systems Biology and Physiology Reports
- Том: 1
- Номер: 1
- Год издания: 2021
- Издательство: Globustar Formaat
- Местоположение издательства: Tallinn, Estonia
- Номер статьи: 3
- DOI: 10.52455/sbpr.01.202101013
- Аннотация: Despite an extensive literature on statistical methods and their proper application to biological data, incorrect analyses remain a critical and widely spread problem in research papers. Inherently hierarchical (nested, clustered) structure of biological measurements is often erroneously neglected, leading to pseudo-replication and false positive results. This, in turn, complicates the correct assessment of statistical power and impairs optimal planning of experiments. In order to attract more attention to this problem and to illustrate the importance of direct account for the nested structure of biological data, in this article we present a simple open-source simulator of two-level normally distributed stochastic data. By defining ‘true’ mean values and ‘true’ intra- and inter-cluster variances of the simulated data, users of the simulator can test various scenarios, appreciate the importance of using correct multi-level analysis and the danger of neglecting the information about the data structure. Here we apply our nested data simulator to highlight some commonly arising mistakes with data analysis and propose a workflow, in which our simulator could be employed to correctly compare two nested groups of experimental data and to optimally plan new experiments in order to increase statistical power when necessary.
- Добавил в систему: Свешникова Анастасия Никитична