follow CCP

Recent blog entries
popular papers

What Is the "Science of Science Communication"?

Climate-Science Communication and the Measurement Problem

Ideology, Motivated Cognition, and Cognitive Reflection: An Experimental Study

'Ideology' or 'Situation Sense'? An Experimental Investigation of Motivated Reasoning and Professional Judgment

A Risky Science Communication Environment for Vaccines

Motivated Numeracy and Enlightened Self-Government

Ideology, Motivated Cognition, and Cognitive Reflection: An Experimental Study

Making Climate Science Communication Evidence-based—All the Way Down 

Neutral Principles, Motivated Cognition, and Some Problems for Constitutional Law 

Cultural Cognition of Scientific Consensus
 

The Tragedy of the Risk-Perception Commons: Science Literacy and Climate Change

"They Saw a Protest": Cognitive Illiberalism and the Speech-Conduct Distinction 

Geoengineering and the Science Communication Environment: a Cross-Cultural Experiment

Fixing the Communications Failure

Why We Are Poles Apart on Climate Change

The Cognitively Illiberal State 

Who Fears the HPV Vaccine, Who Doesn't, and Why? An Experimental Study

Cultural Cognition of the Risks and Benefits of Nanotechnology

Whose Eyes Are You Going to Believe? An Empirical Examination of Scott v. Harris

Cultural Cognition and Public Policy

Culture, Cognition, and Consent: Who Perceives What, and Why, in "Acquaintance Rape" Cases

Culture and Identity-Protective Cognition: Explaining the White Male Effect

Fear of Democracy: A Cultural Evaluation of Sunstein on Risk

Cultural Cognition as a Conception of the Cultural Theory of Risk

« Culturally polarized Australia: Cross-cultural cultural cognition, Part 3 (and a short diatribe about ugly regression outputs) | Main | Even more on motivated consequentialist reasoning »
Sunday
Sep022012

I love Bayes -- and you can too!

No truism is nearly so elegant as, or responsible for more deep insights than, Bayes's Theorem.

I've linked to a couple of teaching tools that I use in my evidence course. One is a Bayesian calculator, which Kw Bilz at UIUC first came up with & which I've tinkered with over time.

The second is a graphic rendering of a particular Bayesian problem. I adapted it from an article by Spiegelhalter et al. in Science

In my view, the "prior odds x likelihood ratio = posterior odds" rendering of Bayes is definitely the most intuitive and tractable. It's really hard to figure out what people who use other renderings are trying to do besides frustrate their audience or make them feel dumb, at least if they are communicating with those who aren't used to manipulating abstract mathematical formuale.  As the graphic illustrates, the "odds" or "likelihood ratio" formalization, in addition to being simple, is the one that best fits with the heuristic of converting the elements of Bayes into natural frequencies, which is an empirically proven method for teaching anyone -- from elementary school children (or at least law students!) to national security intelligence analysts-- how to handle conditional probability.

If you don't get Bayes, it's not your fault.  It's the fault of whoever was using it to communicate an idea to you.

References

Sedlmeier, P. & Gigerenzer, G. Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General 130, 380-400 (2001).

 

 

PrintView Printer Friendly Version

EmailEmail Article to Friend

Reader Comments (4)

thanks for your links, Prof.

September 3, 2012 | Unregistered CommenterChuanpeng Hu

Another device is here:

http://www.sas.upenn.edu/~baron/900/bayes.xm.

This is not self-explanatory. It is for classroom demos by a teacher who explains it, and for students who have heard and read the explanation.

And to my knowledge it works properly only in Firefox. You can change the numbers and see how they affect the different cells and the posteriors.

September 3, 2012 | Unregistered CommenterJon Baron

@Jon Baron: cool! Thanks! Notwithstanding what I said about "likelihood ratio" rendering in post, I recognize that in fact there is surprising amount of heterogeneity in comprehension styles when it comes to graphic (& nongraphic) presentation of data. Probably the lesson of the empirical work here is -- come w/ a well-stocked heuristic toolkit & keep fishing around until you find the one that fits the nut. Pretty sure that's what Spiegelhalter would say!

@Chuanpeng Hu-- you are most welcome!

September 3, 2012 | Registered CommenterDan Kahan

Take logarithms and call it 'information'.

The information post-experiment is the information you have prior to the experiment plus the information you get from the experiment (the log likelihood ratio).

Information theory gives us some of the deepest insights in physics. It's a little more distant from probabilities, so you could argue about just how intuitive it is, but if you squint your eyes...

September 3, 2012 | Unregistered CommenterNiV

PostPost a New Comment

Enter your information below to add a new comment.

My response is on my own website »
Author Email (optional):
Author URL (optional):
Post:
 
Some HTML allowed: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <code> <em> <i> <strike> <strong>