beyond subjective and objective in Statistics
This paper by Andrew Gelman and Christian Hennig calls for the abandonment of the terms objective and subjective in (not solely Bayesian) statistics. And argue that there is more than mere prior information and data to the construction of a statistical analysis. The paper is articulated as the authors’ proposal, followed by four application examples, then a survey of the philosophy of science perspectives on objectivity and subjectivity in statistics and other sciences, next to a study of the subjective and objective aspects of the mainstream statistical streams, concluding with a discussion on the implementation of the proposed move.
“…scientists and the general public celebrate the brilliance and inspiration of greats such as Einstein, Darwin, and the like, recognizing the roles of their personalities and individual experiences in shaping their theories and discoveries” (p.2)
I do not see the relevance of this argument, in that the myriad of factors leading, say, Marie Curie or Rosalind Franklin to their discoveries are more than subjective, as eminently personal and the result of unique circumstance, but the corresponding theories remain within a common and therefore objective corpus of scientific theories. Hence I would not equate the derivation of statistical estimators or even less the computation of statistical estimates to the extension or negation of existing scientific theories by scientists.
“We acknowledge that the “real world” is only accessible to human beings through observation, and that scientific observation and measurement cannot be independent of human preconceptions and theories.” (p.4)
The above quote reminds me very much of Poincaré‘s
“It is often said that experiments should be made without preconceived ideas. That is impossible. Not only would it make every experiment fruitless, but even if we wished to do so, it could not be done. Every man has his own conception of the world, and this he cannot so easily lay aside.” Henri Poincaré, La Science et l’Hypothèse
The central proposal of the paper is to replace `objective’ and `subjective’ with less value-loaded and more descriptive terms. Given that very few categories of statisticians take pride in their subjectivity, apart from a majority of Bayesians, but rather use the term as derogatory for other categories, I fear the proposal stands little chance to see this situation resolved. Even though I agree we should move beyond this distinction that does not reflect the complexity and richness of statistical practice. As the discussion in Section 2 makes it clear, all procedures involve subjective choices and calibration (or tuning), either plainly acknowledged or hidden under the carpet. Which is why I would add (at least) two points to the virtues of subjectivity:
- Spelling out unverifiable assumptions about the data production;
- Awareness of calibration of tuning parameters.
while I do not see consensus as necessarily a virtue. The following examples in Section 3 are all worth considering as they bring more details, albeit in specific contexts, to the authors’ arguments. Most of them give the impression that the major issue stands with the statistical model itself, which may be both the most acute subjectivity entry in statistical analyses and the least discussed one. Including the current paper, where e.g. Section 3.4 wants us to believe that running a classical significance test is objective and apt to detect an unfit model. And the hasty dismissal of machine learning in Section 6 is disappointing, because one thing machine learning does well is to avoid leaning too much on the model, using predictive performances instead. Furthermore, apart from Section 5.3, I actually see little in the paper about the trial-and-error way of building a statistical model and/or analysis, while subjective inputs from the operator are found at all stages of this construction and should be spelled out rather than ignored (and rejected).
“Yes, Bayesian analysis can be expressed in terms of subjective beliefs, but it can also be applied to other settings that have nothing to do with beliefs.” (p.31)
The survey in Section 4 about what philosophy of sciences says about objectivity and subjectivity is quite thorough, as far as I can judge, but does not expand enough the issue of “default” or all-inclusive statistical solutions, used through “point-and-shoot” software by innumerate practitioners in mostly inappropriate settings, with the impression of conducting “the” statistical analysis. This false feeling of “the” proper statistical analysis and its relevance for this debate also transpire through the treatment of statistical expertises by media and courts. I also think we could avoid the Heisenberg principle to be mentioned in this debate, as it does not really contribute anything useful. More globally, the exposition of a large range of notions of objectivity is as often the case in philosophy not conclusive and I feel nothing substantial c28omes out of it… And that it is somehow antagonistic with the notion of a discussion paper, since every possible path has already been explored. Even forking ones. As a non-expert in philosophy, I would not feel confident in embarking upon a discussion on what realism is and is not.
“the subjectivist Bayesian point of view (…) can be defended for honestly acknowledging that prior information often does not come in ways that allow a unique formalization” (p.25)
When going through the examination of the objectivity of the major streams of statistical analysis, I get the feeling of exploring small worlds (in Lindley‘s words) rather than the entire spectrum of statistical methodologies. For instance, frequentism seems to be reduced to asymptotics, while completely missing the entire (lost?) continent of non-parametrics. (Which should not be considered to be “more” objective, but has the advantage of loosening the model specification.) While the error-statistical (frequentist) proposal of Mayo (1996) seems to consume a significant portion [longer than the one associated with the objectivist Bayesianism section] of the discussion with respect to its quite limited diffusion within statistical circles. From a Bayesian perspective, the discussions of subjective, objective, and falsificationist Bayes do not really bring a fresh perspective to the debate between those three branches, apart from suggesting we should give up such value loaded categorisations. As an O-Bayes card-carrying member, I find the characterisation of the objectivist branch somehow restrictive, by focussing solely on Jaynes‘ maxent solution. Hence missing the corpus of work on creating priors with guaranteed frequentist or asymptotic properties. Like matching priors. I also find the defence of the falsificationist perspective, i.e. of Gelman and Shalizi (2013) both much less critical and quite extensive, in that, again, this is not what one could call a standard approach to statistics. Resulting in an implicit (?) message that this may the best way to proceed.
In conclusion, on the positive side [for there is a positive side!], the paper exposes the need to spell out the various inputs (from the operator) leading to a statistical analysis, both for replicability or reproducibility, and for “objectivity” purposes, although solely conscious choices and biases can be uncovered this way. It also reinforces the call for model awareness, by which I mean a critical stance on all modelling inputs, including priors!, a disbelief that any model is true, applying to statistical procedures Popper’s critical rationalism. This has major consequences on Bayesian modelling in that, as advocated in Gelman and Shalizi (2013) , as well as Evans (2015), sampling and prior models should be given the opportunity to be updated when they are inappropriate for the data at hand. On the negative side, I fear the proposal is far too idealistic in that most users (and some makers) of statistics cannot spell out their assumptions and choices, being unaware of those. This is in a way [admitedly, with gross exaggeration!] the central difficulty with statistics that almost anyone anywhere can produce an estimate or a p-value without ever being proven wrong. It is therefore difficult to perceive how the epistemological argument therein [that objective versus subjective is a meaningless opposition] is going to profit statistical methodology, even assuming the list of Section 2.3 was to be made compulsory. The eight deadly sins listed in the final section would require expert reviewers to vanish from publication (and by expert, I mean expert in statistical methodology), while it is almost never the case that journals outside our field make a call to statistics experts when refereeing a paper. Apart from banning all statistics arguments from a journal, I am afraid there is no hope for a major improvement in that corner…
All in all, the authors deserve a big thank for making me reflect upon those issues and (esp.) back their recommendation for reproducibility, meaning not only the production of all conscious choices made in the construction process, but also through the posting of (true or pseudo-) data and of relevant code for all publications involving a statistical analysis.
This entry was posted on August 28, 2015 at 12:15 am and is filed under Books, Statistics, University life with tags academic journals, Basic and Applied Social Psychology, Dennis Lindley, Error-Statistical philosophy, falsification, frequentist inference, Henri Poincaré, Karl Popper, Keep calm posters, Marie Curie, objective Bayes, p-values, refereeing, reproducible research, subjective versus objective Bayes. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.