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原文連結
論文資訊
- 類型：已發表論文
- 日期：2009
摘要
A long-standing question in 生態學 is whether interspecific competition affects co-occurrence patterns of 物種. Null model tests of presence-absence data (NMTPAs) constitute an important approach to address the question, but different tests often give conflicting results when applied to the same data. Neyman-Pearson hypothesis testing theory provides a rigorous and well accepted framework for assessing the validity and optimality of 統計 tests. Here, I treat NMTPAs within this framework, and measure the robustness and bias of 72 representative tests. My results indicate that, when restrictive assumptions are met, existing NMTPAs are adequate, but for general testing situations, the use of all existing NMTPAs is unjustified-the tests are nonrobust or biased. For many current applications of NMTPAs
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