[PoSt] Philsophy of Statistics

[vinux]

Probably I would be the wrong person to start a discussion like philosophy of statistics. Understanding models, programming, and bit of problem solving were my main interest to learn statistics. I never cared about details of the inference or philosophy aspect. I realized that it would be time consuming if I started to understand all statistical models, exploring all aspect of certain programming languages, technological updates ... etc. I would say we can't avoid those. It is possible that introducing a new model or acquire specific skill define a career path.
I consider the best way is by knowing about the abstract or philosophy side. Because this makes perfection on any of our attempts.

I recently read two articles on philosophy side. Technologist to Philosopher (http://chronicle.com/article/From-Technologist-to/128231/) and A Socratic dialogue on mathematics (http://cms.math.ca/10.4153/CMB-1964-044-3). I found it interesting and motivating(second one mainly).

I would like to have discussion on PoSt. I actually believe that there is no difference in PoSt and PoS(philosophy of science). We could follow classic articles/books/personal experience to start our discussion.
I am not sure which is the best medium to discuss philosophy related questions. I feel group -Philosophy of statistics (http://www.talkstats.com/group.php?groupid=13) would be a nice choice. Already bugman and I are posted one question in the group.

Please post your suggestions.

[noetsi]

I spent years studying ontology and epistimology (which is what you are discussing I think). Mine can be boiled down to, if I want to eat I need to run this model correctly. Not as interesting as say logical positivism, but it works as you get older.

There are actually two major elements of this dispute traditionally. Positivism versus post-positivism (which deals primarily with the nature of knowledge, but commonly how you achieve it) and how science changes, notably Kuhn's paradigm.

Good luck on this one :p

[SmoothJohn]

I'm a philosopher who dabbles in statistics, and I'm very interested in this discussion. FWIW, I think Kuhn is the most over-represented, misinterpreted voice in the Philosophy of Science in the past half-century. But we can talk about that later ;)


As far as suggestions go, I am intrigued by the book you suggest. You may also want to consider Ian Hacking's (2006) The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference (2nd Ed.) (http://www.amazon.com/The-Emergence-Probability-Philosophical-Probabilistic/dp/0521685575/ref=pd_sim_b_2).
John

[vinux]

Thanks noetsi, Smooth john. I am relatively new to this subject. I heard about kuhn and poppers work on philosophy of science. I will try to read that now.

I guess I get the Ian hacking's book from our library. It is nice that Amazon also started preview of the book.

[noetsi]

Kuhn was not dealing with statistics per se (not sure he ever worked in that area). He was talking about how knowledge is achieved or perhaps better about how basic understandings of reality, basic paradigms changed particularly in science. The ongoing dispute in that area (well one as there are many) is whether knowledge is acquired incrementally or in dramatic revolutions. To me Kuhn comes down squarely in both camps arguing essentially that understanding of reality slowly changes and then at a certain point there is a dramatic change, one tied however to this build up. In my area of research, public management, this is called punctuated equilibrium. Note however, that is my interpretation of him and I don't come from a camp that stresses theoretical or philisophical approaches.

We want to make the trains run on time and aren't all that confident of in depth understanding of reality, of basic framework that ungirds research. I think statistics in the broad range of academic discourse tends to that as well. Broad theories of statistical reality appear to me (as a non-statistician) to be of limited importance. The focus is more on creating methods that achieve some limited purpose - philosophy is less emphasized.

I think the hard sciences, which I put statistics into, has been less influenced by the attack on positivism that has dominated academics since the sixties. But a lot of that I think is limited interest in such philisophical issues among statisticians. Dason will probably tell me I am crazy :p

[bryangoodrich]

vinux, are you on the TS dropbox share yet? I have something there you might like to read.

Anyway, people talk about PoS they usually spit out positivism, Popper, and Kuhn. I agree that Kuhn is highly misinterpreted. I think he had a brilliant perspective on PoS, but his work was largely best exemplified by the historical synopses he did. People who criticize him tend to focus on wacky ideas like incommensurability (to which Feyerabend seemed to focus more on, to me). Kuhn's real contribution as I see it is identifying the historical role of institutional changes in the scientific culture like the inclusion of measurement and quantification in experimental design. Lakatos sort of did the same thing from a more abstract view in the development of theoretical ideas (especially in mathematics).

In any case, the issues with positivism, etc., aren't really important in modern philosophy of science. The idea that we could rigorously lay out science in some formal terms is clearly ridiculous. The more interesting subject is trying to identify the place of subjectivism in science--what role does interpretation play in the conclusions we draw from scientific experiments?

Noetsi is right that knowledge is an important part of PoS. However, that sort of sweeps a grand amount of detail under the rug and dispatches the interplay between ontology and epistemology: e.g., does having a justifiable methodology that allows us to trust the conclusions of experiments as relaying knowledge about the phenomena under investigation also entail that we're making ontological assertions? Again, interpretation plays a big role here because both in terms of method, theory, and pragmatics, the results of any experiment can be interpreted in numerous ways and the very theoretical foundation that supports a methodology presumes certain things about the way the world is (we don't want to draw circular conclusions here!). The whole issue of paradigms and revolution is neither here nor there. Frankly, most philosophers in this area I've read agree that whatever dramatic shifts we've seen were in part due to large changes in scientific methodology. Of course you can talk about dramatic changes when you move from non-measurement to measurement scientific methods or you can talk about incommensurable interpretations when comparing Greek theorizing to modern scientific theories. But I'd rather stay away from getting into the details of that.

I don't know what would be considered a philosophy of statistics, largely because statistics is not necessarily a theoretical science. It is a methodology. It's theory is mathematical. It's practice can be taken as an art, in some regards. When we talk about the philosophy of biology or physics, we're talking squarely about the theories on which they are founded. Now, I am happy to jump on the philosophy of mathematics bandwagon. It's still something I'm interested in majoring in. But let's be clear, there are some distinct differences between philosophy of science as a study of the practice of science and the philosophy of statistics as a study of the practice of, say, data inquiry. I say data inquiry because if we look at statistics mathematically, we're really just going to sit here talking about mathematical/probability theory. That's not to say there aren't interesting questions, but I think there are distinct topics. I don't think that the topics that are of interest to the philosopher of science or of mathematics are really pertinent to those of statistics, because where PoSt comes in, we're really talking about PoS with statistical/inductive methodology. Difference.

With that said, the interests we have of statistics as a methodology with its relationship to the philosophy of science are squarely found in the philosophy of science. Already mentioned was the great Ian Hacking. He's done great work on inductive methods in science. Wesley Salmon has also explored methodology, but with an emphasis on causality (see Causality and Explanation (http://www.amazon.com/Causality-Explanation-Wesley-C-Salmon/dp/0195108647)). Then it goes without saying that Nancy Cartwright is especially lucid in explaining the way methods, especially statistical and econometric, play a role in science (take your pick, I haven't been let down by any of her books! But a good start is How the Laws of Physics Lie (http://www.amazon.com/How-Laws-Physics-Nancy-Cartwright/dp/0198247044/), and an advanced read is Hunting Causes and Using Them (http://www.amazon.com/Hunting-Causes-Using-Them-Approaches/dp/052167798X/). We can also get pretty specific, like the philosopher of biology, Elliott Sober, does a good review of statistics role in interpreting facts in the first chapter of Evidence and Evolution (http://www.amazon.com/Evidence-Evolution-Logic-Behind-Science/dp/0521692741). I also found Hausman's anthology on The Philosophy of Economics (http://www.amazon.com/Philosophy-Economics-Anthology-Daniel-Hausman/dp/0521709849/) useful in exploring related topics with an economic emphasis.

The point of this listing is that if we're going to talk about PoSt, then we might want to do so in terms of methodology as has been the focus in all these areas of the philosophy of science. This doesn't diminish its importance or the interesting topics that can be covered, as the books above detail. We just have to respect what role statistics plays in scientific inquiry and how that can be explored in the philosophy of science. We can certain get abstract and talk about inductive reasoning (Hacking is good at this, among others both classic and contemporary) or we can get specific and focus on statistics as a mathematical inquiry. That realm of inquiry, though, should be made explicit when we talk about the various topics to follow, whatever those may be.

Addendum: There are a few particularly statistical questions that arise regarding statistical methodology and (inductive) inference. I'm reminded of this by Sober's work. The most important being the role of evidence in our inferences. But this isn't necessarily statistical, per se. It's brought up in pretty much every book I listed above. Where it's particularly statistical as in Sober's book is that evidence (data) plays a role through our methodology to give us a certain basis for drawing conclusions (e.g., confidence intervals). The more obvious one is the philosophical position we take regarding our interpretation of method, which in statistics is usually summed up as the frequentist vs Bayesian positions (I like Sober's focus on likelihoods). This doesn't deviate from my premise that PoSt is a look at method, but it does reveal there are important philosophical issues involving knowledge and interpretation about that method qua method. I don't think we can take these issues as philosophical questions of theory, per se, because there is no theoretical basis that the method is Bayes or about frequency. You can take one method, one conclusion, and simply interpret it from both positions sometimes. Neither position provide a theory that draws that interpretation for us. This, however, requires a bit of depth regarding what a theory means in a technical and philosophical sense.

[vinux]

Bryan, I have shared two articles in the Dropbox. Thanks for the reference.

I am more interested in philosophy discussion in the statistical literature. It would definitely be about modelling approach or modelling explanation. I would love to see PoS problems we face in our workplace. I would reply to you all once I read the basics of PoS.

[bryangoodrich]

So I noticed. Thanks! Comprehensive anthology there.

Anyway, it begs an important question that PoS problems arise in practice ('in the workplace'). That is, unless you work at the forefront of statistical methodology where you need to justify new ground! That is not to say we cannot do as you are interested in, discuss modeling approaches and their explanations. But running up against the various ways we can interpret our models and the larger body of philosophy involving the science we are in (for me that tends to be social and economic) are really 2 different issues, which is what I was driving at. There are concretely statistical issues to talk about (e.g., should we take a frequentist or Bayesian interpretation of our results? Should we use methods specific to those paradigms?) and then there are the broader PoS topics (e.g., does this model reveal something causal or about the truth of economic phenomena as seen in the world?) or we could go very abstract (can inductive reasoning actually provide new knowledge about the world?).

So when you say "in our workplace," what do you have in mind? The more practical issues we face regarding our models or the broader issues behind the assumptions of our scientific enterprise and methods?

[vinux]

I do research in a management institution. So I get chance to meet scholars in humanities, public policy, economics,..., etc. I attend some of their conferences and sometime I feel silly about their experiment and analysis. May be I am not good at qualitative analysis. Anyway I used to get chance to involve in debates. I have posted one question (http://www.talkstats.com/group.php?discussionid=1279&do=discuss)in the group. I am a novice in the area of PoS. I am not sure those has anything to do with philosophy.

[bryangoodrich]

I like the article you put in there. It takes a look at statistics abstracted from where its methods are applied (e.g., science or law) and focuses on the ways in which our assumptions and interpretations can influence the method as method (and ties it together at the end). For instance, he talks about the construction of a probability model, and many philosophers from Carnap to Hacking have lots to say on this fact alone. Is probability subjective? What about physical probability (there's only absolute outcomes--either it happened or it didn't: 0 or 1)? I think he begs a question in the abstract in saying "Inference is only of value if it can be used." What does he mean by "used" here? It's sophistry if it means the inference plays a role thereafter (e.g., in supporting a theory), and it seems wrong if we're just going to say an inference is only valuable when it's pragmatic. Nevertheless, it looks to be a good bit of research. I agree that statistics (theoretically) relies nearly entirely on probability, which is largely (as far as I'm concerned) mathematical. Nevertheless, even in mathematics the connection to "uncertainty of what" has to be asked, and that's where we see these philosophical paradigms arise. Uncertainty in a (mathematical) vacuum is moot. But do those probability models apply everywhere? Do they apply to subjective situations (subjective priors?) or physical (absolute outcomes) reality? These questions, I argue, require the broader PoS that I alluded to earlier. This is why I see statistics and any PoSt to be focused on methodology qua method, not as theory. Where that arises, we move immediately into non-statistical realms about how we interpret information or assumptions we make about the world, but I digress.

[SmoothJohn]

Bryan, I think there is some sense to beginning with apparently naive questions.

1. When we "do" statistics, what are we doing?

2. Do statistics tell us something about the world, or something about our way of dealing with the world? Is there a difference?

3. Do statistics place different ethical demands on us than do other types of information?

These are very basic, but nontrivial questions.

[SmoothJohn]

Just made a quick trip to the *excellent* online resource Stanford Encyclopedia of Philosophy (plato.stanford.edu). There are nice entries on Interpretations of Probability (http://plato.stanford.edu/entries/probability-interpret/) and Bayesian Epistemology (http://plato.stanford.edu/entries/epistemology-bayesian/) both of which I'd be most interested in pursuing in greater detail.

John

[noetsi]

I don't know what would be considered a philosophy of statistics, largely because statistics is not necessarily a theoretical science. It is a methodology. It's theory is mathematical. It's practice can be taken as an art, in some regards.

That somes things up pretty well. But statistics is unusual in that, while arguably a branch of mathematics, many are involved in it who are not mathematicians. Therefore, unusually for an academic arena, the theory of statistics gets less emphasis I think even among many academicians - something rare in my observation. Statistics deals with how things occur primarily, not why nor is there a grand underlying meta theory behind it - at least one that gets discussed much. But again I am not a statistician and only rarely read pure statistical journals. So I may be entirely off base :p

[bryangoodrich]

Bryan, I think there is some sense to beginning with apparently naive questions.

1. When we "do" statistics, what are we doing?

2. Do statistics tell us something about the world, or something about our way of dealing with the world? Is there a difference?

3. Do statistics place different ethical demands on us than do other types of information?

These are very basic, but nontrivial questions.

I agree, they are nontrivial, but to my point, these are distinctly statistical questions as opposed to PoS questions. I mean, you could naively ask those of any activity: When we "do" art, what are we doing? (Philosophy of aesthetics). I find questions like those to be distinctly about the practice of the subject matter, like one can study computer ethics without really delving into any sort of computability theory or talk about bioethics without the philosophy of biology. I think some of these questions can be quite interesting, but those you listed are not :P

To note, I am biased in that my primary interest in the philosophy of science involves methodology and the way interpretation influences our theories (or is coextensive with them from a structuralist perspective). This area of statistics is pretty much entirely subsumed under the philosophy of mathematics (see structuralism in mathematics). Where statistics is concerned, I think I am most interested in its place as an applied mathematics and the way inductive methods can lead to new information.

[noetsi]

I think statistics, like business, suffers from conflict between those who simply want to use it as a tool and theoriest. The former (I am certainly one) don't care why it works as long as it works. We have limited interest in broader theory or the essential assumptions behind statistics as a whole (we do care for obvious reasons if assumptions that influence results are violated, but that tends to be a narrowly technical definition of such violations). At heart I believe most who use statistics are positivist, they believe the results are reality (that is you can use statistics to generate reality ignoring the problems that post-positivist raise in that). I believe this is generally true of the hard sciences to which I think statistics generally belongs.

I have not seen in statistics anything like the broader theories that seek to undergird other academic fields. It focuses on solving problems not meta assumptions or grand theory.

I have not seen, although admitedly I operate at a pretty low level in statistics, anything that challenges positivism in statistics or even something like the uncertainty principal in physics that suggest much is not knowable. Statisticians would likely agree that the state of the art does not allow definite conclusions in some areas, not that this is inherently impossible.

[quark]

This article from StatsBlogs seems to be relevant.

E.S. Pearson’s Statistical Philosophy
http://www.statsblogs.com/2012/08/16/e-s-pearsons-statistical-philosophy/

[SmoothJohn]

Just out of curiosity, what do you guys mean by "positivism"? I see it used as a whipping boy in lot's of social science discourse, but it is rarely made explicit enough for decent analysis.

John

[noetsi]

Positivism was the dominant intellectual paradigm in most research areas until the late sixties (and arguably remains so in practice although rejected commonly in theory). Essentially it asserts that reality is defined by what is discoverable through scientific method. If knowledge is not discovered through scientific method than (depending on the author) it either does not exist or is not valid knowledge. Inherent in this, although not part of the philosophy per se, is what is called scientific method - essentially ways we conduct analysis. It includes rules such as letting the data not opinion decide what is correct, avoiding "subjective" analysis, the use of null hypothesis (which have philisophical not just practical elements to it) and so on.

The post-positivist critique is complex and varied. One element of it argues that it is impossible to avoid subjectivity in analysis, because human beings are inherently subjective. Similarly, it argues that it is impossible to let the data answer questions (critical to positivism's focus on objectivity) because even basic steps such as which data you gather and what questions you ask are subjective. Qualitative analysts argued that many of the rules such as random sampling were invalid for both methodological and philisophical reasons. In general this critique notes that largely hidden in positivism are central assumptions such as support for the existing state of knowledge that are not without challenge - certainly that were judgements as compared to being certain.

I barely touched on this topic, its incredibly complex. It reached its heights I think from the sixties through the eighties. Today I would guess that most researchers who pay attention to such are post-positivist in theory although commonly positivist in the way they do their research. Beyond the issues I have raised, their is the added related issue of the status in society of researchers and research.

[SmoothJohn]

Thanks noetsi. I have serious doubts that positivism was ever expressed so baldly or believed so naively by any philosopher. It seems at best a caricature of epistemological realism.

[noetsi]

I was not trying to claim that it was believed quite that way (although my comments do reflect the views of many of its critics). :p Just sumarizing some of the central elements in a very general way as expressed by academics - few of whoom were philosophers. I lack the training in honesty in philosophy to express it precisely. Logical positivist, people like Herman Simon took truly extreme positions, most I am sure did not. You might look at (on opposite extremes) the statements of Herman Simon and (as a critic) Lincoln and Guba.

As is often the case in academics there were pratical elements at play here. Positivism had many useful aspects to the social sciences which felt itself slighted in the public mind (and in funding) relative to the hard sciences. It (many academics believed) increased the legitimacy of social science research if they were conducting "real" scientific research. And to do so, it was felt, they had to use scientific methods (notably statistical analysis) and apply what social scientists felt were the values and behaviors of hard scientists. Whether the hard sciences actually used those methods as rigerously as was perceived in the social sciences is questonable, but it was felt they were in the heyday of positivism which ran roughly from the late forties to mid sixties. This was not concidently an era with extreme confidence in the use of scientific method to address social issues and the beginning of the revolution in computers which made the analysis possible.

It has been argued, to me with some validity, that it is inherently in academics advantage to support positivism. If there is no objective way to do research, then researchers lack the ability to generate answers that are "better" (and thus more legitimate) in society relative to those that lack the specialized academic training. This of course reduces the validity of research, and particularly social science research, in the general public. By claiming they had a unique, more effective way to generate knowledge researchers were ablet to increase the possibility their research would be accepted and decrease the validity of non-academic commentators. Essentially they were claiming through positivism that they had the only legitimate way to discover reality. Then to empirical research is very hard to justify generally if you don't accept positivist tenets.

So even those who reject positivism, which is likely most academics today, in practice conduct research as if scientific method was correct.

You might look at the history of "behaviorialism" the form of positivism that applied to political science and related fields. It reflects well the difference between politics, practical issues, and philosophy in the academic community. A community that is loath to admit it conducts research in fundamentally subjective ways... :p

If you think about the assumptions on this board, it is pretty easy to see just how deeply positivism is held to even today. Largely of course without question. Do most here believe that statistical method is inherently objective, that accepting the status quo in theory until disproven is the proper way to do analysis, that statisticans understand reality better than non-statisticans, that rarefied objective facts exist seperate from the meaning attached to them in society? Without really questioning any of that? Probably.

I know I do, and I am not even a statistician.

[bryangoodrich]

I think the way Noesti explains positivism borders on scientism. A prominent feature of positivism is merely that knowledge can only be obtained through science as the sole source of authentic information due to its ability to be positively verified. This begs a ton of questions about verification in science. Nonetheless, it was an important part of the social scientific development because it looked the very trite notion that "the natural sciences are successful and can positively verify their results through scientific method; social science can use scientific method to verify their results." Notice how this begs a huge questions about how scientific theories can be verified by the instance of any experiment, and it assumes a continuum between physical and social phenomena in a very big way. Extending this, but certainly within its own lineage, came the logical positivists that sought to stay away from the sort of ontological commitments that make positivism seem so much like scientism in the way Noesti expounded--i.e., that because the only authentic knowledge is scientific, science is the only source of reality. This implies that ontology is founded in epistemology, which it need not be. Logical positivists instead focus on logic as the foundation of scientific reasoning and inference. They accept the positivist epistemology, make no ontological commitments, but only seek to find logical foundations. The key in the logical positivist philosophy is not verification per se, but logical certainty in our assertions. In fact, this is a bolder claim than the positivists could make! It is widely accepted that Karl Popper did a good job dislodging PoS from this view by recognizing the importance of falsification. In particular, this led PoS to look at "what is science?" and try to find what merits are required for an activity to be considered "scientific." Positive verification can never lead one to justify their theory. It is entirely question begging or moot. Of course, the extreme of Popper's position is that we cannot make any verification, only falsification, and all that is necessary is falsification. Kuhn, Lakatos, Lauden, and Feyerabend, among others, critiqued this over the decades revealing that our view of what is "science" is quite diverse and how one goes from evidence to theory and back again is not as objective and rigorous as the logical positivists would have us believe: there is no inductive logic. These postpositivist critiques are not uniform because it's like trying to say atheism as an anti-doctrine of religion is an ideology. It isn't, it's just a rejection of one. Postmodern critiques, however, do have certain features that are important to notice as they are a result of specifically critiquing the positivist and early responses. They highlight that facts don't speak for themselves and they must be interpreted. This requires semantic assumptions about our ways in which we analyze information. Method does not happen in a vacuum. It comes from a semantic structure with a provided syntax that defines what is grammatically correct and what each piece of the vocabulary means. Not every fact, therefore, is objective, because it is biased by the default position one takes regarding the facts, the assumptions behind their models of the world and theory, what other information they have been exposed to, and the community of science at that time. Kuhn especially focused on the cultural aspects of science, and that has played out as a large part of the postmodernist critique, as many people who use it as a pejorative usually do so in the sense that they're implying you think "anything goes because it's whatever people collectively say is true." That's obviously a strawman and ignores the significance of the critique. But now that I've ran along this train of thought for this long, I'll leave you all to

tl;dr

[noetsi]

I was actually talking more about how philosophy has actually been applied historically in the social sciences than the philosophy itself (which I am much less aware of). What positivism is in a purely intelectual sense is very different than what the term has meant in the social sciences - that is what academics in those areas meant when they spared over positivism. This may well be one of those concepts (like culture) that changed its nature fundamentally when it was grafted from one area (in this case philosophy) to method (in the social sciences).

[bryangoodrich]

That is true noetsi. My friend has been more involved in the social sciences than I have (getting her master degrees in economics and political science--international relations, specifically). From reading some of her books and my own studies in the social science, at least in the textbook perspective, they do tend to make a point about positivism. Historically it did play a significant role in the way social scientists perceived their field. This is quite different from positivism in PoS as a study of philosophy. This is true of all PoS, though. The work Sober does in the philosophy of biology, for instance, doesn't necessarily reflect any great trends in actual biological work or the culture thereof. Similarly, the work of Cartwright hasn't particularly driven econometrics anywhere nor has any work of philosophers in physics driven the way we perceive ontological claims brought out by the latest works at CERN. These are all facets of actual scientific work performed by scientists. Though, to be fair, that line has blurred at times and especially by the training of the scientists and the extent of theoretical work they have done (e.g., Friedman and Keynes made considerable contributions to what we would now call the philosophy of economics while actually being working economists, but let us not forget economics is a very young field to which everybody making serious contributions were at the fore!).

I think this example should be considered significantly given that if we are discussing the philosophy of statistics, we have to be clear are we talking about it as a philosophy or merely speculating on the theorizing of practicing statisticians? They both have merits, and we're certainly all more involved with the latter, but the former is most aptly what I would think this thread is about.

[noetsi]

Philosophy tends to be its own distinct world in academics. A central feature of positivism as applied to the social sciences after the second world war was to move away from philosophy to empirical analysis (which positivist in social science suggested was possible - that is basing answers purely on empirical investigation was possible and desirable). This is one of the things that gets hammered by post(anti) positivist of course and with good reason. It is impossible to conduct empirical research apart from theory and it would make little sense to do so.

Again I think it is critical to understand that positivism as applied to social sciences had much to do with politics, those who drove it believed that academics would have more respect and funding if it was perceived as a science (by which they meant an emphasis on empirical data and scientific method). And, in the context of the forties and fifties that was a reasonable view. I don't think that philosophy per se had much to do with this aside from a view like Herbert Simon and his impact had little to do with his logical positivism which few were aware of.

My guess is that most academic statisticians pay little attention to the underlying issues we are talking about. Because scientific method and empiricalism is so well accepted as to be invisible in their world (as in the hard sciences generally). It is only in areas like the social sciences where it was new and commonly not accepted that positivism became a major issue.

[GretaGarbo]

When I first saw this thread I thought that it would be about the difference in statistical “philosophy” between Fisher, Neyman-Pearsson and Baysians. So I looked at a paper by Lemann that you can find here ( http://www.phil.vt.edu/dmayo/PhilStatistics/Other/Lehmann%201993%20%20Fisher%20and%20NP%20theories%20of%20testing%20%20hypotheses%20one%20theory%20or%20two.pdf ) .

The Lemann paper is interesting in itself but there was also mentioned a few books about “real” philosophy. I am just trying to read the latest one, but I can't really say if the book is good or bad. I don't know how much I understand either. Probably less then I think. Now, it is more true than ever that “all I know is that I know nothing” (quoted from a famous person).

I just want to share the refs for these books:

Seidenfeld, T. (1979) Philosophical problems of Statistical Inference, Boston

Kyburg H. E. Jr. (1974) The Logical Foundations of of Statistical Inference, Boston

Hacking, I. (1965) Logic of Statistical Inference, New York

Braithwaite, R. B. (1979) Scientific Explanation, Cambridge

-----

Lemann, E. L. (1993) The Fisher, Neyman-Pearsson Theories of Testing Hypothesis: One Theory of Two, JASA

[vinux]

Thanks Greta for the references.

I have recently read novel by author of Dilbert, God's debris (http://www.amazon.com/Gods-Debris-Experiment-Scott-Adams/dp/0740747878) (Reading the sequel now). Easy read and nice one (trinker may hate, Jake may like). Law of probability is the main theme of the novel. Not dealing with any statistical concepts except using the name "probability".

[vinux]

After reading some PoS article, now I am able to follow this thread. Now I understand the jargon like epistemology, ontology, positivist, naturalist,realism, holism,.... It was very difficult to grasp in the beginning because nature of the topic. I realize that I need to unlearn some of the high school science. The debatable questions like "what is science ?" "pseudo science". "social science", and problems in categorizations were too confusing initially. Best part is reading again and again (same article) gave me lot of insights. Currently I am reading book by Rosenberg, Philosophy of social science (http://www.amazon.com/Philosophy-Social-Science-Alexander-Rosenberg/dp/0813343518)

Now come back to statistics. There is a wiki page on philosophy of statistics (http://en.wikipedia.org/wiki/Philosophy_of_statistics) we could also add philosophy of probability (http://en.wikipedia.org/wiki/Philosophy_of_probability)in our discussions. I found the references are useful (some of them were already mentioned in the previous posts). I have started reading the articles one by one. I will try to answer some of the smoothjohn's questions (http://www.talkstats.com/showthread.php/27461-PoSt-Philsophy-of-Statistics?p=91376&viewfull=1#post91376).

There is a philosophy (http://philosophy.stackexchange.com/) forum in stackexchange. Philosophy of mathematics is discussed there. As of now I haven't seen PoSt related questions. I hope there would be many of such questions in the future.

I found some of the hacking lectures in youtube. Here (http://www.youtube.com/watch?v=ZE94nNB2WOc) is one and there are more (http://www.youtube.com/results?search_query=ian+hacking).

[vinux]

Very Useful information on intersection of philosophy and statistics.

1. Philosophy of Statistics (http://www.phil.vt.edu/dmayo/PhilStatistics/) (Course offered by Deborah G Mayo (http://www.phil.vt.edu/dmayo/personal_website/))
2. Philosophy and the practice of Bayesian statistics (with all the discussions!) (http://andrewgelman.com/2013/02/philosophy-and-the-practice-of-bayesian-statistics-with-discussion/) (Andrew Gelman)

[bryangoodrich]

I don't know if I mentioned it before, but to me I think philosophy of statistics has a solid place in the epistemology studied in the various philosophy of sciences, because (1) statistics is used primarily in the various sciences, and (2) statistics is principally concerned with obtaining information ("knowledge"), usually through inference. That's simplistic, I know, but my point is that when exploring philosophy, be cognizant of things not about statistics directly, but which are still relevant. I've mentioned Elliot Sober's book before. It's basically a book about epistemology in the area of biology (philosophy of biology). The entire first chapter is about statistics, though. I've been reading a bit of philosophy of economics stuff, and I think it is also relevant since there is a lot of talk about causality (can economics achieve it?), and causality in statistics or inductive inference is an important topic. That raises another point, a lot of the time they don't even talk about statistics when they are talking about statistics. Usually they generalize to induction, inductive inference, or inductive logic when talking about the same thing, essentially. One thing I find interesting is the role statistics plays in theory development. By theory in philosophical terms, I mean "a collection of statements that explain a body of disparate phenomena." Moreover, we might append to this definition that theories are truth-seeking. By this I mean that we don't simply take those statements to be true or serve a pragmatic (see "predictive") purpose, but that those statements are true about the world (phenomena) that they explain. This is a point of consternation in economics because often the theory is making false statements, particularly when they're behavioral--e.g., people do not behave rationally in the way economics demands for their theory to be true about markets.

Anyway, I could lecture for days about philosophy of economics :D Thinking about it now, it's kind of disgusting how many books on it I've read (especially if I include PoS generally) lol

My current reading material: http://www.amazon.com/Preference-Choice-Welfare-Daniel-Hausman/dp/1107695120

[vinux]

I have recently gone through some of the articles on genrel laws and causality. Until the "smoking debate" attitude of statistics was not used to establish causality. Fisher or statisticians in the beginning of 20th century considered "statistics is only about association". Last 3-4 decades, different causality establishing techniques developed in statistics. Rubin causal model, structural equation models, granger causality are few of them. counter-factual definition and/or time lag analysis are the key elements to establish the causality. It is not statistics alone is used to establish the causality. Statistics and the context mechanism (experience based and mechanistic) is used to establish the relationship.

I yet to read PoSt articles. Still spending time on PoS articles. Planning to read some articles related to my research (rationality; fall under philosophy of economics).

I found following paragraph (by FA Hayek) interesting.


How little statistics can contribute, however, even in such cases, to the explanation of complex phenomena is clearly seen if we imagine that computers were natural objects which found in sufficiently large numbers and whose behavior we wanted to predict. It is clear that we should never succeed in this unless we possessed the mathematical knowledge built into computers, that is, unless we knew the theory of determining their structure. No amount of statistical information on the correlation between input and output would get us any nearer our aim. Yet the efforts which are made on a large scale with regard to the much more complex structures which we call organisms are of the same kind. The belief that it must be possible in this manner to discover by observation regularities in the relations between input and output without the possession of an appropriate theory in this case appears even more futile and naive than it would be in the case of the computers.
It is from a 1978 article named "The theory of complex phenomena"

[bryangoodrich]

I thought this would be an interesting read. While it is not directly about PoSt, it is about PoS and the relationship between philosophy and the sciences as practiced. This is an important issue, as I put on my philosopher hat, because I find great value in philosophy. However, I also can sympathize with the arguments that philosophy has fallen behind by not keeping up with the sciences. I think a small subset of naturalist philosophy is really the only route to keeping philosophy alive with respect the sciences. Otherwise, it's really nothing but an adjunct to the literary and historical disciplines. While that can be valuable, I find it's greatest merit in its relationship to application, particularly to the sciences. Now, I am a bit biased, since my focus in philosophy began with ethics and ended with the philosophy of science, particularly philosophy of mathematics, philosophy of economics, and rationality. All of these have been partly with respect to abstract thinking, but largely with regard the fact it's looking at the forest instead of the trees that comprise these scientific disciplines I'm passionate about. While the discourse on PoSt is light, the consequences to a divorce of philosophy and science apply just as focused on PoSt as other areas that could be lumped into the philosophy of science.

Enjoy

http://www.guardian.co.uk/science/life-and-physics/2013/mar/24/higgs-philosophy

[bryangoodrich]

Since I'm using this Feedly app on my phone to subscribe to the only RSS feed I read, I've been keeping up on some of the articles that get updated or added to the Stanford Encyclopedia of Philosophy (SEP). Today I read a very nice one that lays out a topic I'm very interested in that has direct application to PoSt. The author makes a point to distinguish this topic from issues in inductive logic. Instead, this requires an understanding of formal logic and probability theory, because the point of probability logic is to take classical (propositional) logic, at the very least, and include probability functions to track uncertainty (error) propagation. Very interesting, especially when you start looking at more complex logics that are available (modal, prepositional, relevant, and more).

http://plato.stanford.edu/entries/logic-probability/

If anybody can track down these articles from one of my top five favorite philosophers (he teaches just a few hours from me too v_v), I'd love to read them!

Van Fraassen, Bas. 1981. Probabilistic Semantics Objectified: I. Postulates and Logics. Journal of Philosophical Logic, 10: 371–391.
–––. 1983. Gentlemen's Wagers: Relevant Logic and Probability. Philosophical Studies, 43: 47–61.
–––. 1984. Belief and the Will. Journal of Philosophy, 81: 235–256.

If anybody is wondering who my other in the top 5 philosophers are, in no particular order: John Stuart Mills, Nancy Cartwright, Thomas Kuhn, and John Searle. I'd have to throw Amartya Sen in there, but I don't know if he's every been considered a philosopher, proper. He's an economist from that classical tradition that is highly theoretical and philosophical, which is why I love his work.

[noetsi]

Probably a good thing I never went to Stanford. I got lost on the 2nd sentence above :p

[noetsi]

Until the "smoking debate" attitude of statistics was not used to establish causality.

They still aren't :p People decided instead that it was likely that smoking caused cancer based on the odds. Only in the last decade or so has it become clear (well somewhat clearer perhaps) what the physical process of smoking causing cancer. Which was tied to medical research/discovery not statistics. If you can't 1) create a theory to explain something and 2) show some of the basic mechanisms involved (at least in theory) you really don't have a basis to claim a causal link regardless of statistics.

Just ask Justice Scalia who just last year argued statistics can never be proof because there could always be an alternative cause :)

[bryangoodrich]

I think the point is that people just didn't think of statistics as supporting causal claims. Their efficacy is another matter. But you cannot separate "medical research" from statistics considering the design of those experiments on many cases is a product of statistics! On a philosophical note, there is much in the way of how theoretical models can justify causal claims. The key here is that you can never prove them. You merely justify the claim. Theory itself just provides an explanation of how that causal relationship works. But it is entirely possible that this explanation is accurate in prediction but not how the mechanics actually work. Something else is required to make a theory (explanation) actually how the phenomena occurs.

[bryangoodrich]

Since I am God-Master of PoST now, I figured I should share this gem I was told about last week. Along with the Stanford Encyclopedia of Philosophy (SEP), this is going to become one of my major sources of relevant research. It's focused on the philosophy of science and contains many pre-prints. For somebody like me that doesn't have access to JSTOR or some of the other closed-access aggregators, this is a valuable resource. If I find stuff on PoST, I'll definitely pass it along from there. I've subscribed to the RSS for 2013 so I'll always see the latest additions. I do the same with SEP to see new entries and revisions made.



http://philsci-archive.pitt.edu/

[bryangoodrich]

I guess this properly would go here: http://philsci-archive.pitt.edu/9774/1/nonprobchance.pdf


"Chance" crops up all over philosophy, and in many other areas. It is often assumed -- without argument -- that chances are probabilities. I explore the extent to which this assumption is really sanctioned by what we understand by the concept of chance.


I'll say a few words once I find the time to read it. The abstract is utterly lacking in content, so it makes me question how good the content will actually be. Nevertheless, there's your PoSt reading for this week! ;)

[spunky]

i know this may sound weird... but are there any VLOGS like youtube stuff preferably about this? i've started to realize that cardio sessions are great learning moments for me if i can just find the right podcast or video to listen to...

[bryangoodrich]

Oh I know, that's how I've been getting my cardio all year. I bike at the gym watching Coursera lectures!

Personally, I prefer reading about philosophy. Often lectures don't do it justice and are better for discussions. I do enjoy when prominent philosophers give keynote speeches and such, but in the philosophy of science? Eh, that's not too common! I'll keep an eye open for any videos I come across, though.

[spunky]

i caught this documentary like WAAAAAAAY a few years back on 4am re-runs and found it fascinating. then i saw that youtube has it as well.

does it look/sound interesting? you're right, ideally one should be able to read and reflect on what is being read... unfortunately (at least for me) real life says otherwise so i need to make-do with whatever i can...

http://www.youtube.com/watch?v=cG3sfrK5B4E

[bryangoodrich]

Hey, I've done a lot of philosophy and economics reading on a treadmill! Of course, then you're not working out very hard. Tradeoffs!

[bryangoodrich]

And that series I found on YouTube out of order. I put it in correct order (to watch straight through) on my YouTube account. Hilary is a rare philosopher. He could do a good job explaining PoS. I might find random clips and make a playlist. Could be a useful resource on the future.

[spunky]

I might find random clips and make a playlist

O-M-G do it! and i'll become your first subscriber! and in exchange i can share with you the most epic lolcatz videos you've ever seen.. :D