![]() Statistics: In statistical modeling, the terms bias and variance measure the accuracy of an estimator. Table 1: Validity and reliability in different disciplines and research paradigms As part of this examination, we realized that each component of data science interprets the terms validity and reliability differently, as presented in Table 1 and as explained below. Specifically, we examine, from the educational perspective, the essence of the components of data science (i.e., statistics, computer science, and the application domain), as well as their interrelations. The interdisciplinarity of data science is one of the characteristics of data science whose implementation for data science education we study. The main concern of reliability is whether the research tool measurements, the data analysis results, and the research findings are persistent. The main concern of validity is whether the research tools indeed measure what they are intended to measure, and whether the data analysis results and findings represent the real world from which data were collected. In the context of research, the terms validity and reliability refer to the level of accuracy and truthfulness of the data collection tools, the data analysis, and the findings (Brink, 1993).
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