Robust Statistical Methods for Empirical Software Engineering
Abstract 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 ai...
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Gespeichert in:
| Autor*in: |
Kitchenham, Barbara [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Empirical software engineering - Springer US, 1996, 22(2016), 2 vom: 16. Juni, Seite 579-630 |
|---|---|
| Übergeordnetes Werk: |
volume:22 ; year:2016 ; number:2 ; day:16 ; month:06 ; pages:579-630 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10664-016-9437-5 |
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OLC2071663667 |
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| 520 | |a Abstract 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. | ||
| 650 | 4 | |a Empirical software engineering | |
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| 650 | 4 | |a Robust methods | |
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| 700 | 1 | |a Charters, Stuart |4 aut | |
| 700 | 1 | |a Gibbs, Shirley |4 aut | |
| 700 | 1 | |a Pohthong, Amnart |4 aut | |
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10.1007/s10664-016-9437-5 doi (DE-627)OLC2071663667 (DE-He213)s10664-016-9437-5-p DE-627 ger DE-627 rakwb eng 004 VZ Kitchenham, Barbara verfasserin aut Robust Statistical Methods for Empirical Software Engineering 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Empirical software engineering Statistical methods Robust methods Robust statistical methods Madeyski, Lech aut Budgen, David aut Keung, Jacky aut Brereton, Pearl aut Charters, Stuart aut Gibbs, Shirley aut Pohthong, Amnart aut Enthalten in Empirical software engineering Springer US, 1996 22(2016), 2 vom: 16. Juni, Seite 579-630 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:22 year:2016 number:2 day:16 month:06 pages:579-630 https://doi.org/10.1007/s10664-016-9437-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 2 16 06 579-630 |
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10.1007/s10664-016-9437-5 doi (DE-627)OLC2071663667 (DE-He213)s10664-016-9437-5-p DE-627 ger DE-627 rakwb eng 004 VZ Kitchenham, Barbara verfasserin aut Robust Statistical Methods for Empirical Software Engineering 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Empirical software engineering Statistical methods Robust methods Robust statistical methods Madeyski, Lech aut Budgen, David aut Keung, Jacky aut Brereton, Pearl aut Charters, Stuart aut Gibbs, Shirley aut Pohthong, Amnart aut Enthalten in Empirical software engineering Springer US, 1996 22(2016), 2 vom: 16. Juni, Seite 579-630 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:22 year:2016 number:2 day:16 month:06 pages:579-630 https://doi.org/10.1007/s10664-016-9437-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 2 16 06 579-630 |
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10.1007/s10664-016-9437-5 doi (DE-627)OLC2071663667 (DE-He213)s10664-016-9437-5-p DE-627 ger DE-627 rakwb eng 004 VZ Kitchenham, Barbara verfasserin aut Robust Statistical Methods for Empirical Software Engineering 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Empirical software engineering Statistical methods Robust methods Robust statistical methods Madeyski, Lech aut Budgen, David aut Keung, Jacky aut Brereton, Pearl aut Charters, Stuart aut Gibbs, Shirley aut Pohthong, Amnart aut Enthalten in Empirical software engineering Springer US, 1996 22(2016), 2 vom: 16. Juni, Seite 579-630 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:22 year:2016 number:2 day:16 month:06 pages:579-630 https://doi.org/10.1007/s10664-016-9437-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 2 16 06 579-630 |
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10.1007/s10664-016-9437-5 doi (DE-627)OLC2071663667 (DE-He213)s10664-016-9437-5-p DE-627 ger DE-627 rakwb eng 004 VZ Kitchenham, Barbara verfasserin aut Robust Statistical Methods for Empirical Software Engineering 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Empirical software engineering Statistical methods Robust methods Robust statistical methods Madeyski, Lech aut Budgen, David aut Keung, Jacky aut Brereton, Pearl aut Charters, Stuart aut Gibbs, Shirley aut Pohthong, Amnart aut Enthalten in Empirical software engineering Springer US, 1996 22(2016), 2 vom: 16. Juni, Seite 579-630 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:22 year:2016 number:2 day:16 month:06 pages:579-630 https://doi.org/10.1007/s10664-016-9437-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 2 16 06 579-630 |
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10.1007/s10664-016-9437-5 doi (DE-627)OLC2071663667 (DE-He213)s10664-016-9437-5-p DE-627 ger DE-627 rakwb eng 004 VZ Kitchenham, Barbara verfasserin aut Robust Statistical Methods for Empirical Software Engineering 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Empirical software engineering Statistical methods Robust methods Robust statistical methods Madeyski, Lech aut Budgen, David aut Keung, Jacky aut Brereton, Pearl aut Charters, Stuart aut Gibbs, Shirley aut Pohthong, Amnart aut Enthalten in Empirical software engineering Springer US, 1996 22(2016), 2 vom: 16. Juni, Seite 579-630 (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:22 year:2016 number:2 day:16 month:06 pages:579-630 https://doi.org/10.1007/s10664-016-9437-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 2 16 06 579-630 |
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Robust Statistical Methods for Empirical Software Engineering |
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Abstract 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. © The Author(s) 2016 |
| abstractGer |
Abstract 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. © The Author(s) 2016 |
| abstract_unstemmed |
Abstract 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. © The Author(s) 2016 |
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| title_short |
Robust Statistical Methods for Empirical Software Engineering |
| url |
https://doi.org/10.1007/s10664-016-9437-5 |
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| author2 |
Madeyski, Lech Budgen, David Keung, Jacky Brereton, Pearl Charters, Stuart Gibbs, Shirley Pohthong, Amnart |
| author2Str |
Madeyski, Lech Budgen, David Keung, Jacky Brereton, Pearl Charters, Stuart Gibbs, Shirley Pohthong, Amnart |
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| doi_str |
10.1007/s10664-016-9437-5 |
| up_date |
2024-07-04T03:57:04.417Z |
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7.399288 |