Lunneborg C E
Department of Statistics, University of Washington, Seattle 98195-4322, USA.
Psychol Methods. 2001 Dec;6(4):402-12.
A frequently used experimental design in psychological research randomly divides a set of available cases, a local population, between 2 treatments and then applies an independent-samples t test to either test a hypothesis about or estimate a confidence interval (CI) for the population mean difference in treatment response. C. S. Reichardt and H. F. Gollob (1999) established that the t test can be conservative for this design-yielding hypothesis test P values that are too large or CIs that are too wide for the relevant local population. This article develops a less conservative approach to local population inference, one based on the logic of B. Efron's (1979) nonparametric bootstrap. The resulting randomization bootstrap is then compared with an established approach to local population inference, that based on randomization or permutation tests. Finally, the importance of local population inference is established by reference to the distinction between statistical and scientific inference.
心理学研究中一种常用的实验设计是将一组可用案例（即一个局部总体）随机分为两种处理方式，然后应用独立样本t检验来检验关于总体处理反应均值差异的假设或估计其置信区间（CI）。C. S. 赖夏德特和H. F. 戈洛布（1999年）证实，对于这种设计，t检验可能会比较保守——得到的假设检验P值过大，或者对于相关局部总体而言置信区间过宽。本文提出了一种对局部总体推断不那么保守的方法，该方法基于B. 埃弗龙（1979年）非参数自助法的逻辑。然后将由此产生的随机化自助法与一种既定的局部总体推断方法（即基于随机化或置换检验的方法）进行比较。最后，通过参考统计推断和科学推断之间的区别，确立了局部总体推断的重要性。
