This article explicates some conceptual and methodological problems involved in the tendency of overreliance on statistical testing logic, logic of disproof. Although statistical hypothesis testing is only a subset of the whole process of empirical testing, a number of researchers tend to misconceive that statistical testing logic is universally applicable to the whole process of empirical theory testing. The authors show that this overreliance on logic of disproof leads to many problems: (1) conceptual problems such as confusion between the research hypothesis and the statistical hypothesis, and oversight of the necessity of logic of proof for accepting the “substantive” null hypothesis containing the researcher’s assertion per se, and (2) methodological problems such as a non-rigorous way of conducting empirical research (i.e., the breakdown of the boundary and the proper sequence between the deductive route and the inductive route in empirical theory testing), an incorrect interpretation of test results associated with the testing of the “substantive” null hypothesis, and confusion between logic of disproof (for testing the null hypothesis) and falsificationism (for testing theory). To remove such conceptual and methodological points of confusion caused by overreliance on logic of disproof, the authors have proposed a seven-step model of empirical theory testing: Step 1 (Theory) → Step 2 (Setting up the research hypothesis: RHEF [i.e., the research hypothesis in existential form]/RHNF [i.e., the research hypothesis in non-existential form]) → Step 3 (Setting up the statistical hypothesis: Translating RHEF or RHNF into H0 and H1) → Step 4 (Testing the statistical hypothesis by logic of disproof/proof) → Step 5 (Testing the research hypothesis: RHEF or RHNF is supported/not supported) → Step 6 (Testing theory empirically: The theory is empirically supported/not supported) → Step 7 (Interpretation of this empirical test result from the researcher’s scientific standpoint, such as falsificationism or logical empiricism) (for better understanding, see Figure 1 in the conclusion section). If the research hypothesis is inductively derived from observations rather than theory, then Step 1, Step 6, and Step 7 become irrelevant. With regard to the application of the seven step model, the authors have noted the following four points. Firstly, researchers should not intend to apply this seven step model to cases where statistical hypothesis testing is not valid (e.g., “H0: The Sun revolves around the Earth” and “H1: The Earth revolves around the Sun” [Johnson, 1999]; “H0: The defendant is innocent” and “Ha: The defendant is guilty” [Anderson et al., 1999]). Secondly, a theory (T), the research hypothesis (RH) derived from the theory, and the statistical hypothesis (H0 and H1) translated from the research hypothesis should be distinguished from each other clearly. Thirdly, even in cases of empirically testable theories or models, two competing theories or models should not be regarded as being the direct objects of statistical testing such as “H0: model A vs. H1: model B” (e.g., “H0: the hypothesized or simpler model” and “H1: the more general model” [e.g., Arora 1982]; “H0: the random-walk model” and “H1: the Markov switching model” (and vice versa) [e.g., Cheung and Erlandsson, 2005]). To test two competing theories or models, they usually have to use two separate seven-step or six-step models. Finally, the former Steps 1-3 correspond to the deductive route ruled by theory and the later Steps 4-6 stand for the inductive route ruled by empirical sample data. Thus, the boundary and the proper sequence between the deductive route and the inductive route must be observed in that order. If researchers observe these four points faithfully, unnecessary confusion can be avoided. The authors believe that the seven step model of empirical theory testing can play a role in accelerating the sound development of scientific knowledge with regard to the empirical testing of theories and hypotheses.

为了消除这些由过度依赖逻辑反证造成的概念和方法论上的混淆，本文作者提出了一个实证理论测试的七步骤模型：第一步（理论) → 第二步（建立研究假设：RHEF[存在形式的研究假设]/RHNF[非存在形式的研究假设] → 第三步（建立统计学假设：把RHEF或RHNF转换为H0 和 H1）→ 第四步（用逻辑反证或逻辑证明来测试统计学假设) → 第五步（测试研究假设：RHEF或RHNF成立或不成立) → 第六步（实证测试理论：该理论实证成立或不成立) → 第七步（从研究者的科学的角度来解释实证测试的结果，例如伪证主义或逻辑经验主义）（为了更好的理解，请参考结论部分的图一）。如果研究假设是通过观察得出而非理论的话，那么第一步，第六步和第七步变得无关紧要。关于这个七步骤模型的运用，作者提出了一下四点。第一，统计假设检验不成立的话，研究者不要使用这个七步模型（例如，“H0: 太阳围绕地球” 和“H1:地球围绕太阳”[Johnson, 1999]; “H0: 被告是无辜的” 和 “Ha: 被告是有罪的” [Anderson et al., 1999])。第二，理论（T），通过理论得出的 研究假设（RH）以及把研究假设转化成的统计学假设之间有显著的区别。第三，即使在理论或模型是可以进行实证测试的情况下，两个对比的理论或模型不应该被看做是统计测试的直接对象。例如“H0: 模型A vs. H1: 模型 B” (比如 “H0: 假设的或较简单的模型” 和 “H1: 更一般的模型” [例如, Arora 1982]; “H0: 无规行走模型” 和 “H1: Markov机制转换模型” (反之亦然) [例如, Cheung 和 Erlandsson, 2005])。要测试两个对比理论或模型，通常需要分别使用七步或六步模型。最后，前三步对应着理论的演绎路线，而后面的第四到第六步代表着实证样本数据的归纳路线。因此，研究者必须有序的观察演绎路线和归纳路线之间的界限和适当的顺序。如果研究者可以忠实的遵守这四点，那么就可以避免不必要的混淆。笔者相信这个实证理论测试的七步骤模型能在理论和假设实证测试方面起到加快理论和假设实证测试方面的科学知识的成熟发展的作用。