Beyond False Positives and Negatives. Understanding Type III and IV Errors in Social Research

Research methodologies that correlate acquired measurement data with critical values defining the null hypothesis, despite their widespread application, remain susceptible to various inference inaccuracies. The decision to reject or fail to reject the null hypothesis can result in type I or type II errors, which can undermine the validity of conclusions. A comprehensive understanding of these errors is crucial for ensuring the accuracy of inferences drawn in social research. Proper definition and control of variables are essential in minimizing the risk of incorrect conclusions. Moreover, examining the broader research context, beyond just statistical probabilities, reveals the potential for additional logical errors (Kimball, 1957). These errors, classified as type III and type IV, have been examined in prior research (Scanlon et al., 1977). This paper seeks to provide an overview of these error types and their definitions. The study identifies several causes of these errors in the conducted analysis, including the selection of statistical methods, the choice of testing area (type III errors), and the neglect of violated assumptions or misinterpretation of interaction effects (type IV errors). The underlying causes of these errors are traced to the research methodology itself, such as improper operationalization, incomplete knowledge of the process under study (type III), and failure to account for study limitations (type IV). In addition, this paper proposes strategies to minimize the occurrence of type III and type IV errors, emphasizing their potential impact and providing recommendations to mitigate these risks.