RSS Discussion Meeting: Beyond subjective and objective in statistics

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RSS discussion paper meetings

Wednesday 12 April 2017, 05:00pm

Location RSS, Errol Street


Andrew Gelman

Andrew Gelman (Columbia University, New York)

Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina.

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.



Christian Hennig

Christian Hennig (University College London)

Dr. Christian Hennig is Senior Lecturer at the Department of Statistical Science, University College London. His research areas are cluster analysis and classification, robust statistics, philosophical foundations of statistics, multivariate analysis, data visualization and statistical modeling. He has given statistical advice to more than 150 clients from various fields including biogeography, astronomy, ecology, sociology, psychology, musicology, economics, chemistry, genetics and information technology. He is the author of more than 50 publications. Journals in which he published include Annals of Statistics, Journal of Multivariate Analysis, Journal of Classification, Foundations of Science, Pattern Recognition, and Systematic Biology. He is Associate Editor of the journals Computational Statistics and Data Analysis, Statistics and Computing, Advances in Data Analysis and Classification, and Statistical Methods and Applications. He is currently the Secretary of the International Federation of Classification Societies. He is one of the editors of the Chapman&Hall/CRC's Handbook of Cluster Analysis.

Dr. Hennig achieved his PhD in 1997 from the Faculty of Mathematics, University of Hamburg for work on clusterwise regression. He worked at the University of Hamburg 1997-2001 and 2001-2003 and habilitated in 2005 on cluster validation and robust clustering. Between 2001 and 2003 he worked at the Seminar for Statistics, ETH Zuerich. Since 2005 he works as Lecturer and Senior Lecturer at the University College London, having also held positions as MSc Programme and Admissions Tutor, and Director of Studies. In 2011 he was visiting researcher at the University of Bologna.


Decisions in statistical data analysis are often justified, criticized or avoided by using concepts of objectivity and subjectivity. We argue that the words ‘objective’ and ‘subjective’ in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability, these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. The advantage of these reformulations is that the replacement terms do not oppose each other and that they give more specific guidance about what statistical science strives to achieve. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgement of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling and socio-economic stratification. The aim of the paper is to push users and developers of statistical methods towards more effective use of diverse sources of information and more open acknowledgement of assumptions and goals.

The preprint is available to access at

Contact Discussion Meeting Committee

Refreshments from 4.30pm

Organiser Name Judith Shorten

Email Address This email address is being protected from spambots. You need JavaScript enabled to view it.

Organising Group(s) .





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