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Principles of Exploratory Data Analysis in Problem Solving

Summary: [This abstract is based on the authors' abstract.] Exploratory data analysis (EDA) is often used as a hypothesis identification approach to problem solving. This study discusses EDA’s three main principles: Display the data, identify salient features, and interpret salient features. The dominant empiricist concept of EDA is criticized and compared to that which emphasizes the role of mental models in hypothesis generation. It is argued that in teaching EDA the emphasis for statistical data analysis should be balanced with teaching students to theorize and question. Ideas are illustrated by a well-known case of the transmission mechanism of cholera.

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  • Topics: Data Quality, Problem Solving
  • Keywords: Art of discovery, Data analysis, Hypothesis testing, Influential observations, Problem solving
  • Author: Mast, Jeroen de; Kemper, Benjamin P. H.
  • Journal: Quality Engineering