Title: The consequences of checking for zero-inflation and overdispersion in the analysis of count data Show Chinese title

Title: 计数数据分析中检查零膨胀和过离散的后果

Abstract: Count data are ubiquitous in ecology and the Poisson generalized linear model (GLM) is commonly used to model the association between counts and explanatory variables of interest. When fitting this model to the data, one typically proceeds by first confirming that the data is not overdispersed and that there is no excess of zeros. If the data appear to be overdispersed or if there is any zero-inflation, key assumptions of the Poison GLM may be violated and researchers will then typically consider alternatives to the Poison GLM. An important question is whether the potential model selection bias introduced by this data-driven multi-stage procedure merits concern. In this paper, we conduct a large-scale simulation study to investigate the potential consequences of model selection bias that can arise in the simple scenario of analyzing a sample of potentially overdispersed, potentially zero-heavy, count data. Show Chinese abstract

Abstract: 计数数据在生态学中无处不在，泊松广义线性模型 (GLM) 常用于建模计数与感兴趣的解释变量之间的关联。当拟合此模型时，通常首先确认数据没有过离散化且不存在过多的零值。如果数据似乎存在过离散化或者存在过多的零值，则泊松 GLM 的关键假设可能会被违反，研究人员通常会考虑替代泊松 GLM 的方法。一个重要问题是，这种基于数据驱动的多阶段过程引入的潜在模型选择偏差是否值得担忧。在本文中，我们进行了大规模的模拟研究，以调查在分析可能存在过离散化和可能有过多零值的计数数据样本的简单情况下，可能出现的模型选择偏差的潜在后果。