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Measuring the Effects of Data Mining on Inference

Measuring the Effects of Data Mining on Inference

Tom Burr, S. Tobin
Copyright: © 2015 |Pages: 9
ISBN13: 9781466658882|ISBN10: 1466658886|EISBN13: 9781466658899
DOI: 10.4018/978-1-4666-5888-2.ch176
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MLA

Burr, Tom, and S. Tobin. "Measuring the Effects of Data Mining on Inference." Encyclopedia of Information Science and Technology, Third Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2015, pp. 1825-1833. https://doi.org/10.4018/978-1-4666-5888-2.ch176

APA

Burr, T. & Tobin, S. (2015). Measuring the Effects of Data Mining on Inference. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Third Edition (pp. 1825-1833). IGI Global. https://doi.org/10.4018/978-1-4666-5888-2.ch176

Chicago

Burr, Tom, and S. Tobin. "Measuring the Effects of Data Mining on Inference." In Encyclopedia of Information Science and Technology, Third Edition, edited by Mehdi Khosrow-Pour, D.B.A., 1825-1833. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-5888-2.ch176

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Abstract

Data mining is a term used to describe various types of exploratory data analysis whose purposes are to select data models, estimate model parameters, and generate hypotheses that can be tested on future data. It is known that model predictions are overly optimistic when generated from the same data that are used to select a model and estimate its parameters. Therefore, most statistical procedures assume that the data model is selected prior to data collection. Alternatively, to adjust for data mining, we describe steps that should be taken to account for “choosing the best” among many candidate models.

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