Measuring the Effects of Data Mining on Inference

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Author(s): Tom Burr (Statistical Sciences, Los Alamos National Laboratory, USA)and S. Tobin (Nuclear Nonproliferation and Security, Los Alamos National Laboratory, USA)
Copyright: 2015
Pages: 9
Source title: Encyclopedia of Information Science and Technology, Third Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-4666-5888-2.ch176

Keywords: Information Science Reference / IT Research & Theory / IT Research and Theory / Library & Information Science

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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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Measuring the Effects of Data Mining on Inference

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