Staff profile

Professor Geoff Cumming FAPS

Emeritus Professor

Faculty of Science, Technology and Engineering

School of Psychological Science
Department of Psychology

George Singer Building, Statistical Cognition Laboratory, Room 461, Melbourne (Bundoora)

 

Qualifications

B Sc, Dip Ed Monash, DPhil Oxford.

Membership of professional Associations

Fellow, Association of Psychological Science

Area of study

Psychology

Brief Profile

I hope my book will change the world:

Cumming, G. (2012). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York: Routledge.  You can download the book's Preface, Contents, and a sample chapter.  (If you are in Australia, you could try booko to get prices for the book, including delivery.)

Psychology and many other disciplines need to improve the way they analyse data.  A vital first step is to use, as much as possible, estimation--meaning confidence intervals--instead of null hypothesis significance testing (NHST) and p values.  NHST is a deeply flawed statistical approach, and the current widespread obsession with NHST, and reliance on NHST, is damaging to research progress.  

I refer to effect sizes, confidence intervals, and meta-analysis as the new statistics not because the techniques are new, but because adopting them would for most researchers be very new, and a major change in thinking.  Such a change is highly desirable, and could greatly improve our research.  The highly influential American Psychological Association Publication Manual now states that researchers should "Wherever possible, base discussion and interpretation of results on point and interval estimates" (p. 34).  That's unequivocal support for the new statistics.

I retired in January 2008, mainly to write the book and develop the software that goes with it.  The software is ESCI ("ESS-key", Exploratory Software for Confidence Intervals).  ESCI and the book are intended to support better understanding of the new statistics, and their use by researchers and students in a wide range of disciplines.  ESCI is a free download.   

Also, I am now looking out for possible short- or medium-length visits to interesting labs in interesting places.  (I can offer research talks and various statistics workshops, as given for example at recent APA Conventions.)

My main current research is in the area of statistical cognition, which is the study of how people understand--or misunderstand--statistical concepts, and various different ways to present the results of statistical analyses.  I advocate the evidence-based practice of statistics, meaning that our selection of a statistical technique should be supported by cognitive evidence that people understand it well. 

I am especially interested in replication, which is the topic of Chapter 5 in the book.  One of many reasons that CIs are better than p values is that CIs generally give quite good information about what is likely to happen on replication of an experiment, whereas a p value gives almost no information about replication.  The dance of the p values illustrates how p values vary enormously with replication, thus indicating how terribly uninformative they are.

 

Recent Publications

Book

Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York: Routledge.

Software package

Cumming, G. (2001-2011). ESCI, Exploratory Software for Confidence Intervals. Computer software, available from: http://www.latrobe.edu.au/psy/research/projects/esci

Refereed publications (journal articles and book chapters)

In the citations below, * denotes that there is an ESCI module available for free download, to accompany this article.

Lai, J., Fidler, F., & Cumming, G. (in press). Subjective p intervals: Researchers underestimate the variability of p values over replication. Methodology. [abstract]

Cumming, G., Fidler, F., Kalinowski, P., & Lai, J. (2011). The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes, confidence intervals, and meta-analysis. Australian Journal of Psychology. doi:10.1111/j.1742-9536.2011.00037.x

Cumming, G., & Fidler, F. (2011). From hypothesis testing to parameter estimation: An example of evidence-based practice in statistics. In A. T. Panter & S. Sterba (Eds.) Handbook of Ethics in Quantitative Methodology (pp. 293-312). New York: Routledge.

Cumming, G. (2010). p values versus confidence intervals as warrants for conclusions that results will replicate. In B. Thompson & R. Subotnik (Eds.) Methodologies for Conducting Research on Giftedness (pp. 53-69). Washington, DC: APA Books.

Cumming, G., & Fidler, F. (2010). Effect sizes and confidence intervals. In G. R. Hancock & R. O. Mueller (Eds.) The reviewer’s guide to quantitative methods in the social sciences (pp. 79-91). London: Routledge. [chapter]

Coulson, M., Healey, M., Fidler, F., & Cumming, G. (2010). Confidence intervals permit, but do not guarantee, better inference than statistical significance testing. Frontiers in Quantitative Psychology and Measurement, 1:26. doi:10.3389/fpsyg.2010.00026. Available online: http://www.frontiersin.org/psychology/quantitativepsychologyandmeasurement/paper/10.3389/fpsyg.2010.00026/  Also available at: tinyurl.com/cisbetter

Cumming, G. (2010). Replication, p-rep, and confidence intervals: Comment prompted by Iverson, Wagenmakers, and Lee (2010); Lecoutre, Lecoutre, and Poitevineau (2010); and Maraun and Gabriel (2010). Psychological Methods, 15, 192-198. doi: 10.1037/a0019521

Kalinowski, P., Lai, J., Fidler, F., & Cumming, G. (2010). Qualitative research: An essential part of statistical cognition research. Statistics Education Research Journal, 9(2), 22-34. Available online: http://www.stat.auckland.ac.nz/~iase/serj/SERJ9(2)_Kalinowski.pdf

Speirs-Bridge, A., Fidler, F., McBride, M., Flander, L., Cumming, G., & Burgman, M. (2010). Reducing overconfidence in the interval judgments of experts. Risk Analysis, 30, 512-523. doi: 10.1111/j.1539-6924.2009.01337.x [article pdf]

Cumming, G. (2009). Inference by eye: Reading the overlap of independent confidence intervals. Statistics in Medicine, 28, 205-220.

Cumming, G., & Fidler, F. (2009). Confidence intervals: Better answers to better questions. Zeitschrift für Psychologie / Journal of Psychology, 217, 15-26.

Finch, S., & Cumming, G. (2009). Putting research in context: Understanding confidence intervals from one or more studies. Journal of Pediatric Psychology, 34, 903-916. doi: 10.1093/jpepsy/jsn118 [download article pdf] *

Cumming, G. (2008). Confidence intervals. In G. Ritzer (Ed.) The Blackwell concise encyclopedia of sociology (pp. 79-80). Oxford, UK: Blackwell.

Beyth-Marom, R., Fidler, F., & Cumming, G. (2008). Statistical cognition: Towards evidence-based practice in statistics and statistics education. Statistics Education Research Journal., 7, 20-39 [download article pdf]

Cumming, G. (2008). Replication and p intervals: p values predict the future only vaguely, but confidence intervals do much better. Perspectives on Psychological Science, 3, 286-300. *

Faulkner, C., Fidler, F., & Cumming, G. (2008). The value of RCT evidence depends on the quality of statistical analysis. Behaviour Research and Therapy, 46, 270-281.

Fidler, F., & Cumming, G. (2008). The new stats: Attitudes for the twenty-first century. In J.W. Osborne (Ed.). Best practice in quantitative methods (pp. 1-12). Thousand Oaks, CA: Sage. [Introduction]

Fidler, F., Faulkner, S., & Cumming, G. (2008). Analyzing and presenting outcomes: Focus on effect size estimates and confidence intervals. In A. M. Nezu & C. M. Nezu (Eds.) Evidence-based outcome research: A practical guide to conducting randomized controlled trials for psychosocial interventions (pp. 315-334). New York: OUP.

Kalinowski, P., Fidler, F., & Cumming, G. (2008). Overcoming the inverse probability fallacy: A comparison of two teaching interventions. Methodology, 4, 152-158. Doi: 10.1027/1614-2241.4.4.152. [abstract]

Velicer, W. F., Cumming, G., Fava, J. L., Rossi, J. S., Prochaska, J. O., & Johnson, J. (2008). Theory testing using quantitative predictions of effect size. Applied Psychology: An International Review, 57, 589-608. [abstract]

Cumming, G. (2007). Confidence intervals. In G. Ritzer (Ed.) The Blackwell encyclopedia of sociology (vol. II, pp. 656-659). Oxford, UK: Blackwell.

Cumming, G. (2007). Inference by eye: Pictures of confidence intervals and thinking about levels of confidence. Teaching Statistics, 29, 89-93.  *

Cumming, G., Fidler, F., Leonard, M., Kalinowski, P., Christiansen, A., Kleinig, A., Lo, J., McMenamin, N., & Wilson, S. (2007) Statistical reform in psychology: Is anything changing? Psychological Science,18, 230-232.

Cumming, G., Fidler, F., & Vaux, D. L. (2007). Error bars in experimental biology. Journal of Cell Biology, 177, 7-11. [link to article]

Fidler, F. & Cumming, G. (2007). Lessons learned from statistical reform efforts in other disciplines. Psychology in the Schools, 44, 441-449.

Cumming, G., & Maillardet, R. (2006). Confidence intervals and replication: Where will the next mean fall? Psychological Methods, 11, 217-227. *

Fidler, F., Burgman, M., Cumming, G., Buttrose, R. & Thomason, N. (2006). Impact of criticisms of hypothesis significance testing on statistical reporting practices in conservation biology. Conservation Biology,20, 1539-1544.

Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods, 10, 389-396.

Cumming, G. (2005). Understanding the average probability of replication. Comment on Killeen (2005). Psychological Science, 16, 1002-1004. * *

Di Stefano, J., Fidler, F., & Cumming, G. (2005). Effect size estimates and confidence intervals: An alternative focus for the presentation and interpretation of ecological data. In A. R. Burk (Ed.) New trends in ecology research (pp. 71-102). New York: Nova Science Publishers.

Fidler, F., Cumming, G., Thomason, N., Pannuzzo, D., Smith, J., Fyffe, P., Edmonds, H., Harrington, C., & Schmitt, R. (2005). Evaluating the effectiveness of editorial policy to improve statistical practice: The case of the Journal of Consulting and Clinical Psychology. Journal of Consulting and Clinical Psychology. 73, 136-143.

Fidler, F., Thomason, N., Cumming, G., Finch, S., & Leeman, J. (2005). Confidence intervals, still much to learn: Reply to Rouder & Morey. Psychological Science, 16, 494-495.

Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals, and how to read pictures of data. American Psychologist, 60, 170–180. [download article] *

Cumming, G. (2005). Megabytes and colour, but learning is still the issue. Australian Educational Computing, 20(1), 14-17.

Fidler, F., Cumming, G., Thomason, N. & Burgman, M. (2004). Statistical reform in medicine, psychology and ecology. Journal of Socio-Economics, 33, 615-630.

Cumming, G., Williams, J., & Fidler, F. (2004). Replication, and researchers’ understanding of confidence intervals and standard error bars. Understanding Statistics, 3, 299-311. *

Finch, S., Cumming, G., Williams, J., Palmer, L., Griffith, E., Alders, C., Anderson, J., & Goodman, O. (2004). Reform of statistical inference in psychology: The case of Memory & Cognition. Behavior Research Methods, Instruments & Computers, 36, 312-324.

Fidler, F., Thomason, N., Cumming, G., Finch, S., & Leeman, J. (2004). Editors can lead researchers to confidence intervals, but can’t make them think: Statistical reform lessons from medicine. Psychological Science, 15, 119-126.

Van Gelder, T., Bissett, M., & Cumming, G. (2004). Cultivating expertise in informal reasoning. Canadian Journal of Experimental Psychology, 58, 142-152. [abstract]

Wolfe, R., & Cumming, G. (2004). Communicating the uncertainty in research findings: Confidence intervals. Journal of Science and Medicine in Sport, 7, 138-143. (Subject of invited editorial: Marshall, S. (2004). Testing with confidence: The use (and misuse) of confidence intervals in biomedical research. Journal of Science and Medicine in Sport, 7, 135-137.) [abstract ] *

Finch, S., Thomason, N., & Cumming, G. (2002). Past and future American Psychological Association guidelines for statistical practice. Theory & Psychology, 12, 825-853.

Cumming, G. (2001). Project Design and achieving educational change: From StatPlay to ESCI. In G. Kennedy, M. Keppell, C. McNaught & T. Petrovic (Eds.), Meeting at the Crossroads. Proceedings of the 18th Annual Conference of the Australian Society for Computers in Learning in Tertiary Education. (pp. 151-160). Melbourne: Biomedical Multimedia Unit, The University of Melbourne. http://www.ascilite.org.au/conferences/melbourne01/pdf/papers/cummingg.pdf

Cumming, G., & Finch, S. (2001). A primer on the understanding, use and calculation of confidence intervals based on central and noncentral distributions. Educational and Psychological Measurement, 61, 530-572. *