Process / pipelinehypothesis-testing
零假设检验
零假设显著性检验（NHST）是经验研究中占主导地位的统计框架。零假设（H₀）代表默认假设——通常是“无效应”或“无差异”——而备择假设（H₁）代表正在检验的声称。检验计算在H₀为真时观察到数据的概率（p值）；如果p值非常小，则拒绝H₀，转而支持H₁。NHST由Ronald Fisher提出，并由Neyman和Pearson在20世纪初扩展，是验证性研究的基础，但因滥用和误解而受到广泛批评。
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来源
- Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗
- Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337. DOI: 10.1098/rsta.1933.0009 ↗
- Gigerenzer, G., & Marewski, J. N. (2015). Surrogate Science: The Idol of a Universal Method for Scientific Inference. Journal of Management, 41(2), 421–440. DOI: 10.1177/0149206314547522 ↗
如何引用本页
ScholarGate. (2026, June 3). Null Hypothesis Significance Testing (NHST) and Hypothesis Formulation. ScholarGate. https://scholargate.app/zh/research-statistics/null-hypothesis
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