Temp Anna G M, Naumann Marcel, Hermann Andreas, Glaß Hannes
Translational Neurodegeneration Section "Albrecht Kossel," Department of Neurology, University Medical Centre, Rostock, Germany.
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Rostock, Germany.
Front Neurol. 2022 Mar 24;13:796777. doi: 10.3389/fneur.2022.796777. eCollection 2022.
Statistical evaluation of empirical data is the basis of the modern scientific method. Available tools include various hypothesis tests for specific data structures, as well as methods that are used to quantify the uncertainty of an obtained result. Statistics are pivotal, but many misconceptions arise due to their complexity and difficult-to-acquire mathematical background. Even though most studies rely on a frequentist interpretation of statistical readouts, the application of Bayesian statistics has increased due to the availability of easy-to-use software suites and an increased outreach favouring this topic in the scientific community. Bayesian statistics take our prior knowledge together with the obtained data to express a degree of belief how likely a certain event is. Bayes factor hypothesis testing (BFHT) provides a straightforward method to evaluate multiple hypotheses at the same time and provides evidence that favors the null hypothesis or alternative hypothesis. In the present perspective, we show the merits of BFHT for three different use cases, including a clinical trial, basic research as well as a single case study. Here we show that Bayesian statistics is a viable addition of a scientist's statistical toolset, which can help to interpret data.
实证数据的统计评估是现代科学方法的基础。可用的工具包括针对特定数据结构的各种假设检验，以及用于量化所得结果不确定性的方法。统计学至关重要，但由于其复杂性和难以掌握的数学背景，产生了许多误解。尽管大多数研究依赖于对统计读数的频率主义解释，但由于易于使用的软件套件的出现以及科学界对该主题的宣传增加，贝叶斯统计的应用有所增加。贝叶斯统计将我们的先验知识与所得数据结合起来，以表达对某一特定事件可能性的置信程度。贝叶斯因子假设检验（BFHT）提供了一种直接的方法来同时评估多个假设，并提供支持原假设或备择假设的证据。在本视角中，我们展示了BFHT在三个不同用例中的优点，包括一项临床试验、基础研究以及一个单病例研究。在这里，我们表明贝叶斯统计是科学家统计工具集的一个可行补充，有助于解释数据。
