- Thread starter trinker
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keep dreaming spunky....

three words for you: SMALL.SAMPLE.SIZES

we just need to market that idea and people will be converting to Bayesianism in no time.

and I shall be become the leader of this new movement of religious Bayesianism predicated as a a quick-fix, one-size-fits-all solution to everything! much like we did with null hypothesis testing... but shinier and with more sequins!

So the couple of hundred real statisticians in the US and Canada might someday run bayes. Very few others will.

So the couple of hundred real statisticians in the US and Canada might someday run bayes. Very few others will.

i know that, once again, you're being silly (well, i'm being silly myself on this. frequentism will never go away) but regarding the comment about sample sizes i always need to remind you that just because you, in your limited micro-universe do something does not imply the vast majority of people do it. and yes, i am very aware that you're prone to just quick, sweeping (mostly unfounded) generalizations (like i'm prone to hystrionics and drama) but that's the way you are and i'm OK with it.

Whether people teaching undergraduate stats classes, as far as most practisoners will go, will shift to a baysian approach I don't know. But I am skeptical this will occur.

Companies and government deal with observational data. And that tends to be very large. My own experiences are beside the point, the structure of data used by firms and most government agencies is large simply because of the nature of what they do. They don't go out and gather data, they are interested in what they do and it is extremely unlikely that many firms are only going to have a few hundred cases to work from. That occurs in academics and experimental design because of the cost and availability of data. Firms gather vast quantity of data and deal with large numbers of units so they will not encounter this by the nature of what they do and analyze.

Do you really think most firms are going to have a hundred or less cases of observational data to analyze? Or that they commonly analyze data that is not observational in nature?

shift to a baysian approach I don't know. But I am skeptical this will occur.

The application of Bayesian stats is already everywhere in modern society, and from smart traffic lights to spam filtering it has proven it's use. It's the computational revolution that made it all possible. It may be rare in some fields, prevalent in others but regardless of how you - or I - personally feel about Bayesian methods, lets not deny that it's use and application are already extensive.

Examples include image processing, signal analysis, phylogenetic trees, network analysis, underwater navigation, court cases and gene expression data.

From my limited experience (my 2 cents) I've seen both frequentist and Bayesian methods produce volumes of output from analyses on the same large data -- thing is, with Bayesian methods there's more room for "tweaking" so that fewer "significant" things are outputed.

Most of these examples involve large volumes of data that I'm sure Fisher never envisioned when developing experimental design in agricultural fields.

From my limited experience (my 2 cents) I've seen both frequentist and Bayesian methods produce volumes of output from analyses on the same large data -- thing is, with Bayesian methods there's more room for "tweaking" so that fewer "significant" things are outputed.

From my limited experience (my 2 cents) I've seen both frequentist and Bayesian methods produce volumes of output from analyses on the same large data -- thing is, with Bayesian methods there's more room for "tweaking" so that fewer "significant" things are outputed.

However also the argument that Bayesian methods are feasible for large observational datasets is beside the point, true born-again Bayesians have compelling arguments to use Bayes on small data as well. These arguments are so compelling that Bayes is becoming the standard in many drug testing trials. A major argument is that you should risk as little patients as possible, and correct application of Bayes apparently helps, you can make more complex inference with less data - and thus less risk of human life.

A recent paper by Johnson makes the case that significance determined with classical test only amounts to marginal evidence (especially with small datasets) and that the wide use of classical tests - were alpha levels of 0.05 - 0.01 are seen as significant - is a major contributor to the appallingly large proportion of study were people fail to reproduce results in some fields. He argues that the application of Bayesian uniform most powerful test would reduce this proportion greatly.

I suspect that a major contribution is also that many classical tests are being applied to study designs that are not appropriate so I am highly skeptical that 'standard' Bayesian tests built for ANOVA type study designs will solve this - but this is again completely besides my point.

I simply do not agree with the notion that Bayes is not widely applied. You may not use it in your field, you may not have seen it during your undergrad courses, but you're seeing it's application in your daily life and likely also using it daily - from your traffic avoidance app to the collision avoidance software in your new truck or the face recognition on your camera. Bayesian methods are already everywhere. That is my only point, the rest are semantics.

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Classical statistics are failing across the board, and Bayesian statistical are filling the hole.

It would be interesting to know how often these methods were taught in undergraduate classes last year. As I noted I have taken graduate classes in stats in a variety of programs and universities and never seen baysian statistics.

It would be interesting to know how often these methods were taught in undergraduate classes last year. As I noted I have taken graduate classes in stats in a variety of programs and universities and never seen baysian statistics.

I'm not sure of course, but I bet that most universities must be giving graduate and undergrad courses in Bayesian statistics. Mine does. Yours didn't,

so our best estimate (MLE) is: 50% of the universities give Bayes classes (N = 2).

It is discouraging to me how rarely I have encountered anyone who did statistics even in 1,000 plus member organizations (which is fairly large by US standards). At any level - forget more advanced approaches like baysian. I am the statistical "expert" at my present large organization - which is a sad commentary on the expertise.

I think people who are experts at stats, and work with others equally familiar, don't have a strong sense of the reality in most non-research operations.

It may well be field specific. I was commenting on social sciences including administration and education.

but think about this noetsi. when multivariate methods started being developed (you know, the good, ol' MANOVA that we all know and love, Principal Components Analysis, etc.) it took not one, not two, but almost 30 years between these methods were published and they started getting taught in graduate programs in Psychology, Sociology and social sciences in general. and they found TREMENDOUS opposition... mostly people arguing that we didn't need any of that and as long as we had multiple regression we were OK.

fast-forward to now and people are regularly taught this thing. it's so common that even SPSS does it.

Bayesian statistics is probably going through a similar process right now. think about it... if this board had existed 30 yrs ago you'd probably be saying "MANOVA? oh pff... who ever needs that? just do regression!"

It woul be interesting to do a formal review of education and psychology journals that use statistics and find out how often baysian approaches are utilized. I would be willing to make a ten dollar bet that it shows up less than 10 percent of the time

I think it is becoming more common in education and psychology particularly now that it is now integrated into Mplus. Like TR says IRT, Multiple Imputations, etc. Bayes has been around in ed and psych for a while now.

OWN your Bayesianism and choose an informative prior!