We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Robust statistical methods for empirical software engineering.

Kitchenham, Barbara and Madeyski, Lech and Budgen, David and Keung, Jacky and Brereton, Pearl and Charters, Stuart and Gibbs, Shirley and Pohthong, Amnart (2016) 'Robust statistical methods for empirical software engineering.', Empirical software engineering., 22 (2). pp. 579-630.


There have been many changes in statistical theory in the past 30 years, including increased evidence that non-robust methods may fail to detect important results. The statistical advice available to software engineering researchers needs to be updated to address these issues. This paper aims both to explain the new results in the area of robust analysis methods and to provide a large-scale worked example of the new methods. We summarise the results of analyses of the Type 1 error efficiency and power of standard parametric and non-parametric statistical tests when applied to non-normal data sets. We identify parametric and non-parametric methods that are robust to non-normality. We present an analysis of a large-scale software engineering experiment to illustrate their use. We illustrate the use of kernel density plots, and parametric and non-parametric methods using four different software engineering data sets. We explain why the methods are necessary and the rationale for selecting a specific analysis. We suggest using kernel density plots rather than box plots to visualise data distributions. For parametric analysis, we recommend trimmed means, which can support reliable tests of the differences between the central location of two or more samples. When the distribution of the data differs among groups, or we have ordinal scale data, we recommend non-parametric methods such as Cliff’s δ or a robust rank-based ANOVA-like method.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution.
Download PDF
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
Download PDF (Advance online version)
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
Download PDF
Publisher Web site:
Publisher statement:© The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Date accepted:04 May 2016
Date deposited:06 May 2016
Date of first online publication:16 June 2016
Date first made open access:04 July 2016

Save or Share this output

Look up in GoogleScholar