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NCJRS Celebrates National Library Week April 12-18

National Library Week

Started in 1958, National Library Week is a nationwide observance celebrated by all types of libraries - including the NCJRS Virtual Library. NCJRS invites you to explore the breadth and scope of the NCJRS Virtual Library collection and services. With more than 220,000 collection documents and 60,000 online resources, including all known Office of Justice Programs works, it is one of the world’s largest criminal justice special collections.

We encourage your Feedback. Tell us how you use the NCJRS Virtual Library and Abstracts Database, how you access the collection, and any ways we can improve our services.

NCJRS Abstract

The document referenced below is part of the NCJRS Library collection.
To conduct further searches of the collection, visit the NCJRS Abstracts Database.

How to Obtain Documents
NCJ Number: NCJ 240702     Find in a Library
Title: The Pitfalls of Prediction
  Document URL: HTML PDF 
Author(s): Greg Ridgeway
  Journal: NIJ Journal  Issue:271  Dated:February 2013  Pages:34 to 40
Date Published: 02/2013
Page Count: 7
  Annotation: This article discusses the advantages of using the latest scientific developments to make reliable predictions about crime.
Abstract: Research has shown that criminal justice researchers, practitioners, analysts, and other stakeholders often make common mistakes in when making predictions about crime and criminals. These common mistakes are 1) trusting expert predictions too much; 2) clinging to what was learned in Statistics 101; 3) assuming one method works best for all problems; 4) trying to interpret too much; 5) forsaking model simplicity for predictive strength – or vice versa; 6) expecting perfect predictions; and 7) failing to consider the unintended consequences of predictions. This article discusses each of these mistakes and how the use of recent scientific developments could be used to avoid them. Some of the ways to avoid these mistake include constructing and using predictive models, be willing to use different prediction methods based on the particular prediction problem, focus on the accuracy of the predictions, and use models that are simple to understand and simple to implement. Figures, table, and notes
Main Term(s): Prediction
Index Term(s): Crime control model ; Research methods ; Models ; Crime prediction ; Criminality prediction
Type: Issue Overview
Country: United States of America
Language: English
  To cite this abstract, use the following link:

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