The strength of evidence versus the power of belief: Are we all Bayesians?

author: Jessica Utts, University of California, Irvine
published: Aug. 9, 2010,   recorded: July 2010,   views: 2738
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Slides

Slides
0:00 The Strength of Evidence versus The Power of Belief : Are We All Bayesians?
2:26 Abstract from Proceedings
3:52 Collaborators for Bayesian Part
4:07 Outline of Talk
4:47 Why This Topic? Some Background
6:15 Why Be a Bayesian? - 1
7:43 Why Be a Bayesian? - 2
8:26 Why Be a Bayesian? - 3
9:31 Why This Topic for ICOTS?
10:10 Psi/Psychic/ESP/Anomalous Cognition
12:15 Controlled experiments to Test ESP
14:34 Remote Viewing Protocol
16:46 Some Aditional Details
17:39 Example of an Excellent Match
18:50 Early Remote Viewing Example
19:31 Target: Pete's Harbor Restaurant
19:38 How to Judge?
21:44 You Judge this Typical Novice Response
22:43 Rank-Order Judging
23:48 Analysis Methods
25:19 Automated Ganzfeld Experiments Similar to Remote Viewing
27:47 Simplest Model for RV and Ganzfeld
28:48 Granzfeld Studies in This Analysis
30:17 Binomial Analysis
31:24 Individual Confidence Intervals
31:49 Combined 95%
31:56 Are You Convinced
33:42 Simple Bayesian Analysis
34:31 How to Determine Beta Prior
35:10 Consider 3 Prior Sets of Belief
36:07 Posterior for p, Skeptic and Believer
36:49 Open-minded: One study and all data
37:37 Summary of Simple analysis
38:10 More Complex
40:09 Bayesian Hierarchical Model
40:31 Some Technical Stuff
40:45 More Technical Stuff
41:10 Prior Distributions
42:12 Results
42:24 Percentiles of Posterior Distribution of Median
43:21 95% Range for Individual p
43:49 Finding about Study-to-study Variation
44:13 Comparing of Bayesian and Frequentist Results
44:49 Some conclusions from the analyses
45:29 Teaching Activities
48:25 Summary

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Description

Although statisticians have the job of making conclusions based on data, for many questions in science and society prior beliefs are strong and may take precedence over data when people make decisions. For other questions, there are experts who could shed light on the situation that may not be captured with available data. One of the appealing aspects of Bayesian statistics is that the methods allow prior beliefs and expert knowledge to be incorporated into the analysis along with the data. One domain where beliefs are almost sure to have a role is in the evaluation of scientific data for extrasensory perception (ESP). Experiments to test ESP often are binomial, and they have a clear null hypothesis, so they are an excellent way to illustrate hypothesis testing. Incorporating beliefs makes them an excellent example for the use of Bayesian analysis as well. In this paper, data from one type of ESP study are analyzed using both frequentist and Bayesian methods.

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Reviews and comments:

Comment1 Jeff, January 10, 2012 at 2:45 a.m.:

I too have to be very skeptical that there is not a file drawer problem going on here. Jessica's analysis really needs to take this into consideration and demonstrate that it is not the reason for this bias to be taken seriously. It is the obvious answer that I had been thinking about from the very first slide, and was a question brought up by the audience. The fact that it is such an obvious explanation that she is aware of, yet she didn't mention it during the speech is even more troubling.

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