The training error can, therefore, be viewed as the validity of the learned model since it was trained on labeled data (that is, known data). The training error is the prediction error on samples that the machine-learning algorithm has trained on, and it reflects the accuracy of the model's representation of the training data. Variance measures the distribution of the values of the estimator around its expected value, and so variance can be seen as analogous to reliability.Ĭomputer science: In machine learning, which was developed mainly in the context of computer science, the terms training error and test error refer to the expected prediction error of the model constructed by a machine-learning algorithm. Bias refers to the distance of the expected value of the estimator from the real value of the estimated parameter, and so it may be seen as analogous to validity. 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).

Data reliability and validity