# Necessary/recommended level of theory for developing statistical intuition

**URL:** <https://discourse.datamethods.org/t/necessary-recommended-level-of-theory-for-developing-statistical-intuition/5648>\
**Category:** statistical\
**Created:** [May 11, 2022, 12:50pm UTC](https://discourse.datamethods.org/t/necessary-recommended-level-of-theory-for-developing-statistical-intuition/5648 "2022-05-11T12:50:05Z")\
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**Author:** ![R\_cubed](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/r_cubed/32/1518_2.png) [@R\_cubed](https://discourse.datamethods.org/u/R_cubed)\
**Post date:** [August 11, 2022, 11:58am UTC](https://discourse.datamethods.org/t/necessary-recommended-level-of-theory-for-developing-statistical-intuition/5648/67 "2022-08-11T11:58:01Z")

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I will merely point out that this quote from Freedman (which I take as representative of your views)

> Blockquote  
> For thirty years, I have found Bayesian statistics to be a rich source of mathematical questions. However, I no longer see it as the preferred way to do applied statistics, because **I find that uncertainty can rarely be quantified as probability.**

is different from this:

> Blockquote  
> The successful users of probability models should consider them simply as fallible tools to help them think of more questions and provide directions for seeking more information and data, not **turn-the-crank machines that provide risk management recipes free of shoe leather and critical thinking.**

No one (especially not **me** ) argued for the latter. To do so would be engaging in the practice of  
“persuasion and intimidation” that Stark properly condemns. Unfortunately, I find much of “evidence based” rhetoric accurately described by his language.

I believe I’ve shown via the example of extracting the implied probabilities from options prices, that even decision makers at the highest levels, in a regime of incomplete markets, find valuable information contained in a subjective, betting odds interpretation of probability that has no physical meaning.

Indeed, I consider **precise** probability models merely as a point of reference to compute the width of the range of **interval probabilities** (aka covering priors) worth considering.

> [@Who should define reasonable priors and how?](https://discourse.datamethods.org/t/who-should-define-reasonable-priors-and-how/2935/13):
>
> I thought I’d bump this thread after finding a valuable discussion between Andrew Gelman and Sander Greenland on this precise issue of setting priors. Specifying a prior distribution for a clinical trial: What would Sander Greenland do? [https://statmodeling.stat.columbia.edu/2008/02/05/specifying\_a\_pr\_1/](https://statmodeling.stat.columbia.edu/2008/02/05/specifying_a_pr_1/) Specifying a prior distribution for a clinical trial [https://statmodeling.stat.columbia.edu/2008/01/24/specifying\_a\_pr/](https://statmodeling.stat.columbia.edu/2008/01/24/specifying_a_pr/) My posts in this thread were some speculations I had about how 2 hone…

**Related Thread**

> [@Frequentist vs Bayesian debate applied to real-world data analysis: any philosophical insights?](https://discourse.datamethods.org/t/frequentist-vs-bayesian-debate-applied-to-real-world-data-analysis-any-philosophical-insights/1695/7):
>
> I think we need to keep in mind the important distinction between correct frequentist statistics, and simply bad statistics. @Sander posted a link to a youtube video (I’ve also posted it here in a few different threads) on p-values, confidence intervals, and their common, but incorrect interpretations. [[Digging Deeper Into p-values: NISS Webinar Follow-up with Three Authors - November, 2020] ](https://www.youtube.com/watch?v=_p0MRqSlYec) It was mentioned in passing in the video that Jeffries and Fisher would pose statistical p…

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