Data and Models
Data and Models
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DOI：
10.1007/978-0-387-74075-1_2
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
David R. Anderson
中科院分区：
文献类型：
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作者：
David R. Anderson
数据应取自适当的概率抽样方案或有效的实验设计，其中也涉及概率成分。这些都是导致一定程度的科学严谨性的重要步骤。这些数据通常来自某种概率抽样，并被称为“代表性”。“在这个理想的框架之外，存在着这样的理想抽样在很大程度上是不可行的人口。例如，人类群体通常由对采样来说是异质的成员组成。因此，根据定义，不可能抽取随机样本，这种异质性可能导致人口规模估计值的负偏差。已经开发出对这种异质性具有鲁棒性的估计器，这些方法已被证明是有用的，但标准误差通常很大。一般来说，必须小心，要么获得合理的代表性样本，或推导出模型和估计，可以提供有用的推论（有时不可避免的）非随机抽样。
Data should be taken from an appropriate probabilistic sampling protocol or from a valid experimental design, which also involves a probabilistic component. These are important steps leading to a degree of scientific rigor. Such data often arise from probabilistic sampling of some kind and are said to be “representative.“ Outside of this desirable framework lie populations where such ideal sampling is largely unfeasible. For example, human populations are often composed of members that are heterogeneous to sampling. Thus, by definition, it is impossible to draw a random sample and such heterogeneity can lead to negative biases in estimators of population size. Estimators that are robust to such heterogeneity have been developed and these approaches have proven to be useful, but the standard error is often large. In general, care must be exercised to either achieve reasonably representative samples or derive models and estimators that can provide useful inferences from (the sometimes unavoidable) nonrandom sampling.