Abt K
Neuropsychobiology. 1983;9(1):47-51. doi: 10.1159/000117937.
More than one variable of interest in pharmaco-EEG studies is the rule rather than the exception. The many-variables situation may arise from several types of EEG variables and/or from repeated measurements of these variables through time. In many cases, multivariate analysis techniques are not suitable for application. As an alternative, the analyst usually applies individual (univariate) tests for all variables and/or time points. This significance testing of many variables causes problems because of the so-called 'alpha-inflation', i.e. the inflation of the probability for the 'error of the first kind' to reject a null hypothesis although it is valid. To counteract this effect (which invalidates the significance levels used for hypothesis testing), various procedures have been proposed some of which are discussed in the present paper. All procedures involve so-called 'alpha-adjustments', and two procedures are based upon the assumption that the investigator demands that at least a given percentage of all individual null hypotheses considered will be rejected or are not valid, respectively.
在药物脑电图研究中，存在一个以上感兴趣的变量是常态而非例外。多变量情况可能源于几种类型的脑电图变量和/或这些变量随时间的重复测量。在许多情况下，多变量分析技术并不适合应用。作为替代方法，分析人员通常对所有变量和/或时间点应用单独的（单变量）检验。对许多变量进行这种显著性检验会产生问题，因为存在所谓的“α膨胀”，即尽管原假设有效，但拒绝原假设的“第一类错误”的概率会膨胀。为了抵消这种效应（这会使用于假设检验的显著性水平无效），已经提出了各种程序，本文将讨论其中一些程序。所有程序都涉及所谓的“α调整”，并且有两种程序基于这样的假设：研究者分别要求在所有考虑的单个原假设中至少有给定百分比被拒绝或无效。
