Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/37564
Title: Against Inferential Statistics: How and why current statistics teaching gets it wrong.
Authors: White, Patrick
Gorard, Stephen
First Published: 1-May-2017
Publisher: International Association for Statistics Education (IASE)
Citation: The Statistics Education Research Journal, 2017, in press
Abstract: Recent concerns about a shortage of capacity for statistical and numerical analysis skills among social science students and researchers have prompted a range of initiatives aiming to improve teaching in this area. However, these projects have rarely re-evaluated the content of what is taught to students and have instead focussed primarily on delivery. The emphasis has generally been on increased use of complex techniques, specialist software and, most importantly in the context of this paper, a continued focus on inferential statistical tests (ISTs), often at the expense of other types of analysis. We argue that this ‘business as usual’ approach to the content of statistics teaching is problematic for several reasons. First, the assumption underlying ISTs are rarely met, meaning that students are being taught analyses that should only be used very rarely. Secondly, all of the most common outputs of ISTs – p-values, standard errors (SEs) and confidence intervals (CIs) – suffer from a similar logical problem that renders them at best useless and at worst misleading. Eliminating ISTs from statistics teaching (and practice) would avoid the creation of another generation of researchers who either do not understand, or knowingly misuse, these techniques. It would also have the benefit of removing one of the key barriers to students’ understandings of statistical analysis.
eISSN: 1570-1824
Links: http://iase-web.org/
http://hdl.handle.net/2381/37564
Embargo on file until: 1-Jun-2017
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2016. After embargo this version will be an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Appears in Collections:Published Articles, Dept. of Sociology

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