Measuring Distribution Similarities Between Samples: A Distribution-Free Overlapping Index
Measuring Distribution Similarities Between Samples: A Distribution-Free Overlapping Index
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DOI：
10.3389/fpsyg.2019.01089
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发表时间：
2019-05-21
影响因子：
3.8
通讯作者：
Calcagni, Antonio
中科院分区：
文献类型：
--
作者：
Pastore, Massimiliano;Calcagni, Antonio
每天，认知和实验研究者都试图通过群体之间的统计差异或相似性来寻找支持他们假设的证据。最典型的情况涉及使用t统计量或其他度量（如Cohen的d或U度量）量化两个样本的平均值差异。在这两种情况下，目的都是量化这种差异必须有多大才能被归类为显著影响。这些问题在处理实验和应用心理学研究时特别相关。然而，大多数这些标准的措施需要一些分布的假设是正确的使用，如对称性，单峰性，以及建立良好的参数形式。虽然这些假设保证了推理的渐近性质得到满足，但它们往往会限制结果的有效性和可解释性。在这篇文章中，我们说明了使用的分布免费重叠措施作为一种替代方法来量化样本差异和评估研究假设表示的贝叶斯证据。通过三个实证应用说明了重叠指数的主要特征和潜力。结果表明，使用该指数可以大大提高心理学研究中数据分析结果的可解释性，以及研究人员可以从他们的研究中得出的结论的可靠性。
Every day cognitive and experimental researchers attempt to find evidence in support of their hypotheses in terms of statistical differences or similarities among groups. The most typical cases involve quantifying the difference of two samples in terms of their mean values using the t statistic or other measures, such as Cohen's d or U metrics. In both cases the aim is to quantify how large such differences have to be in order to be classified as notable effects. These issues are particularly relevant when dealing with experimental and applied psychological research. However, most of these standard measures require some distributional assumptions to be correctly used, such as symmetry, unimodality, and well-established parametric forms. Although these assumptions guarantee that asymptotic properties for inference are satisfied, they can often limit the validity and interpretability of results. In this article we illustrate the use of a distribution-free overlapping measure as an alternative way to quantify sample differences and assess research hypotheses expressed in terms of Bayesian evidence. The main features and potentials of the overlapping index are illustrated by means of three empirical applications. Results suggest that using this index can considerably improve the interpretability of data analysis results in psychological research, as well as the reliability of conclusions that researchers can draw from their studies.