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Damaging Real Lives through Obstinacy: Re-Emphasising Why Significance Testing is Wrong

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  • Stephen Gorard

Abstract

This paper reminds readers of the absurdity of statistical significance testing, despite its continued widespread use as a supposed method for analysing numeric data. There have been complaints about the poor quality of research employing significance tests for a hundred years, and repeated calls for researchers to stop using and reporting them. There have even been attempted bans. Many thousands of papers have now been written, in all areas of research, explaining why significance tests do not work. There are too many for all to be cited here. This paper summarises the logical problems as described in over 100 of these prior pieces. It then presents a series of demonstrations showing that significance tests do not work in practice. In fact, they are more likely to produce the wrong answer than a right one. The confused use of significance testing has practical and damaging consequences for people's lives. Ending the use of significance tests is a pressing ethical issue for research. Anyone knowing the problems, as described over one hundred years, who continues to teach, use or publish significance tests is acting unethically, and knowingly risking the damage that ensues.

Suggested Citation

  • Stephen Gorard, 2016. "Damaging Real Lives through Obstinacy: Re-Emphasising Why Significance Testing is Wrong," Sociological Research Online, , vol. 21(1), pages 102-115, February.
    • Handle: RePEc:sae:socres:v:21:y:2016:i:1:p:102-115
    DOI: 10.5153/sro.3857
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    References listed on IDEAS

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    1. Athena Engman, 2013. "Is there life after P>0.05? Statistical significance and quantitative sociology," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 257-270, January.
    2. Andreas Schwab & Eric Abrahamson & William H. Starbuck & Fiona Fidler, 2011. "PERSPECTIVE---Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests," Organization Science, INFORMS, vol. 22(4), pages 1105-1120, August.
    3. Louis Guttman, 1985. "The illogic of statistical inference for cumulative science," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 1(1), pages 3-9.
    4. Marks R. Nester, 1996. "An Applied Statistician's Creed," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 401-410, December.
    5. Stephen Gorard, 2003. "Understanding Probabilities and Re-Considering Traditional Research Training," Sociological Research Online, , vol. 8(1), pages 104-112, February.
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    Cited by:

    1. Stephen Gorard, 2017. "Significance Testing is Still Wrong, and Damages Real Lives: A Brief Reply to Spreckelsen and Van Der Horst, and Nicholson and McCusker," Sociological Research Online, , vol. 22(2), pages 204-210, May.
    2. James Nicholson & Sean Mccusker, 2016. "Damaging the Case for Improving Social Science Methodology through Misrepresentation: Re-Asserting Confidence in Hypothesis Testing as a Valid Scientific Process," Sociological Research Online, , vol. 21(2), pages 136-147, May.
    3. Thees F Spreckelsen & Mariska Van Der Horst, 2016. "Is Banning Significance Testing the Best Way to Improve Applied Social Science Research? – Questions on Gorard (2016)," Sociological Research Online, , vol. 21(3), pages 95-105, August.

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