Statistical hermeneutics

I started this post a few months ago and it caught my attention as I was looking through drafts. I now can’t remember exactly what instigated the draft, but I’m guessing it was something in the Philosophize This! podcast pointing me to this entry by Theodore George in the Stanford Encyclopedia of Philosophy. I just read quickly through the essay and despite the broad and varied history and use of the term hermeneutics (hərməˈn(y)o͞odiks/), I find something very valuable in considering the term and what its implications could be relative to the study of statistical inference in science. I have alluded to this in other posts, but not said explicitly — a huge aspect of what I see as missing in the use of statistical methods in science is a meta- view, but even bigger than what meta-research often hits on (at least as discussed in a science reform context). I hadn’t before identified what I’m after as meta-interpretation, but that is where hermeneutics leads me and it seems fitting.

Here is George’s introduction to the concept:

Hermeneutics is the study of interpretation. Hermeneutics plays a role in a number of disciplines whose subject matter demands interpretative approaches, characteristically, because the disciplinary subject matter concerns the meaning of human intentions, beliefs, and actions, or the meaning of human experience as it is preserved in the arts and literature, historical testimony, and other artifacts. Traditionally, disciplines that rely on hermeneutics include theology, especially Biblical studies, jurisprudence, and medicine, as well as some of the human sciences, social sciences, and humanities. In such contexts, hermeneutics is sometimes described as an “auxiliary” study of the arts, methods, and foundations of research appropriate to a respective disciplinary subject matter (Grondin 1994, 1). For example, in theology, Biblical hermeneutics concerns the general principles for the proper interpretation of the Bible. More recently, applied hermeneutics has been further developed as a research method for a number of disciplines (see, for example, Moules inter alia 2015)

Stanford Encyclopedia of Philosophy

This leads me to thinking about a type of missing meta study that keeps us from tackling big questions about the role of Statistics within the larger scientific process. The complexities in the interpretation of statistical models, results, predictions, etc. are often downplayed and taken for granted, as well as the downstream effects on science and decision making. It’s just not often that we are hit with an explicit call to discuss and study our, often implicit, interpretations of the statements that come out of our use of statistical methods and results (statements we think of as “interpretation”). So I like the word, despite that fact it doesn’t exactly roll off the tongue, at least at first.

George focuses on its meaning within philosophy, but I don’t see a great distance between his words in the context of Philosophy and those that could be useful in the context of Statistics:

Within philosophy, however, hermeneutics typically signifies, first, a disciplinary area and, second, the historical movement in which this area has been developed. As a disciplinary area, and on analogy with the designations of other disciplinary areas (such as ‘the philosophy of mind’ or ‘the philosophy of art’), hermeneutics might have been named ‘the philosophy of interpretation.’ Hermeneutics thus treats interpretation itself as its subject matter and not as an auxiliary to the study of something else. Philosophically, hermeneutics therefore concerns the meaning of interpretation—its basic nature, scope and validity, as well as its place within and implications for human existence; and it treats interpretation in the context of fundamental philosophical questions about being and knowing, language and history, art and aesthetic experience, and practical life.

The key sentence to me is “Hermeneutics thus treats interpretation itself as its subject matter and not as an auxiliary to the study of something else.” There are times when we focus heavily on interpretation in teaching and practicing Statistics, but it’s rare to go up a level to where we might seriously study interpretation, beyond quantitative and theoretical properties of methods. Hermeneutics would bring in the complex human and social dimensions that are carved into interpretation of statistical results in science, whether we like it or not. And that’s hard for many reasons that I think are obvious.

Here’s my first attempt at translating George’s words from the philosophy-context into a statistical context: Statistical hermeneutics concerns the meaning of interpretation of quantitative results based on statistical approaches – its basic nature, scope and validity, as well as its place within and implications for science; and it treats interpretation in the context of fundamental questions of inference, philosophy of science, history and philosophy of statistics, decision making, and scientific practice.

Maybe statistical hermeneutics can be defined more simply as something like “the study of interpretation of results and conclusions based statistical theory, methods, and reasoning.” I’m sure there’s a better definition waiting for us, but I think this simple one gets us somewhere.

In Statistics courses, we teach how to interpret things like estimated regression coefficients, credible/confidence/compatibility intervals, and p-values. But there is rarely a meta-interpretation layer that honors the disagreements in many of those interpretations, the unsettled foundations of how they should be used in science, and the downstream effects on science and decision making. The meta-interpretation context is probably what I find most fascinating in Statistics and also most challenging, and why I have a hard time fully hopping on board with typical interpretations and find it uncomfortable to carry out analyses of my own — I just never quite buy into the whole process, which I think mostly comes down to my being uncomfortable with the simple explicit interpretations are ultimately interpreted on a deeper and implicit level by the humans doing the processing.

Here is another quote in the essay that particularly jumped out at me for its message being one I think is often missing in the context of Statistics. I also like the positive spin on the finitude of human understanding – which I find myself strongly agreeing with.

Hermeneutics may be said to involve a positive attitude—at once epistemic, existential, and even ethical and political—toward the finitude of human understanding, that is, the fact that our understanding is time and again bested by the things we wish to grasp, that what we understand remains ineluctably incomplete, even partial, and open to further consideration. In hermeneutics, the concern is therefore not primarily to establish norms or methods which would purport to help us overcome or eradicate aspects of such finitude, but, instead, to recognize the consequences of our limits. Accordingly, hermeneutics affirms that we must remain ever vigilant about how common wisdom and prejudices inform—and can distort—our perception and judgment, that even the most established knowledge may be in need of reconsideration, and that this finitude of understanding is not simply a regrettable fact of the human condition but, more importantly, that this finitude is itself an important opening for the pursuit of new and different meaning.

There are also interesting connections to discussions about the validity of Human Science. I won’t go into the details here, but you can find them in the essay under that heading. Here’s just a taste: “In this, Dilthey’s concern is to defend the legitimacy of the human sciences against charges either that their legitimacy remains dependent on norms and methods of the natural sciences or, to his mind worse, that they lack the kind of legitimacy found in the natural sciences altogether.” There is also more on beliefs among philosophers that “modern science, despite all the methodological and technological sophistication, has failed to account for the basic epistemic foundation on which it relies.”

Things that we, as scientists, could benefit from thinking about more often, even if we might disagree.

Section 7 describes Postmodern Hermeneutics and mainly references the work of Lyotard related to the dangers and possibilities of postmodern rejection of “meta-narratives”, where “meta-narratives include, say, stories about the objectivity of science and the contribution that science makes to the betterment of society.” Lyotard sees both “danger and possibility in the postmodern rejection of metanarratives.” 

I barely scratched the surface of philosophy of hermeneutics by reading this one essay – but I see value in continuing to dig into the concept (?) of hermeneutics. The essay makes it clear there are obvious ties to influential philosophical work related to science, but I’m not sure how much it has filtered into the worlds of practicing scientists. It seems to me that there is much that could be done to help scientists grapple with some underlying challenges with use of statistical methods in practice – and how results are ultimately interpreted and used in the process of doing science and contributing to “understanding.”

After writing most of this post, I did a quick search to see if/how others might have combined hermeneutics and Statistics. There are some interesting looking ideas and contributions. I share a few here, but with the disclaimer that I have not yet read them carefully myself (they are now part of my anti-library for the time being). Ironically, maybe there are too many interpretations of hermeneutics to make it directly useful, but I still find myself thinking it could carry something worthwhile.

Paola Gerbaudo on Data Hermeneutics

Robert Groves on Bayesian Statistics and Hermeneutics

Diana Taylor on Hermeneutics, Statistics, and the Repertory Grid

Graham White on Semantics, Hermeneutics, Statistics – and the Semantic Web

Herbert Kritzer on the Nature of Interpretation in Quantitative Research