France Robert L, Heung Brandon
Department of Plants, Food and Environmental Science, Dalhousie University, Truro, Nova Scotia, Canada, B2N 5E3.
J Hazard Mater Adv. 2023 Feb;9:100220. doi: 10.1016/j.hazadv.2022.100220. Epub 2022 Dec 17.
Despite the requirement for data to be normally distributed with variance being independent of the mean, some studies of plastic litter, including COVID-19 face masks, have not tested for these assumptions before embarking on analyses using parametric statistics. Investigation of new data and secondary analyses of published literature data indicate that face masks are not normally distributed and that variances are not independent of mean densities. In consequence, it is necessary to either use nonparametric analyses or to transform data prior to undertaking parametric approaches. For the new data set, spatial and temporal variance functions indicate that according to Taylor's Power Law, the fourth-root transformation will offer most promise for stabilizing variance about the mean.
尽管要求数据呈正态分布且方差与均值无关，但一些关于塑料垃圾的研究，包括新冠疫情期间的口罩，在使用参数统计进行分析之前并未对这些假设进行检验。对新数据的调查以及对已发表文献数据的二次分析表明，口罩的分布并非正态，且方差与平均密度并非无关。因此，有必要要么使用非参数分析，要么在采用参数方法之前对数据进行转换。对于新数据集，空间和时间方差函数表明，根据泰勒幂定律，四次方根变换对于稳定均值周围的方差最有前景。
