Interpretering Bayesian Analysis
Posted: Fri Jul 23, 2021 8:25 pm
Background
On the Interpreter blog, Dr. Kyler Rasmussen Ph.D. has begun a series of posts “summarizing and evaluating Book of Mormon-related evidence from a Bayesian statistical perspective.” While the moderators of the comment section of that website have been very generous allowing me to post my criticisms there, the formatting is subpar. I thought I’d post some comments regarding his methodology here.
To explain my point, I’m going to present a simple example of how Bayesian analysis is supposed to work that should be easy to understand. I’ll then use that as an analogy for what Dr. Rasmussen is doing wrong.
Simple Example
Say I have a coin. I have the hypothesis that the coin is fair, i.e. that the probability of flipping heads is 50%. In contrast, Kyler thinks the coin is weighted, and thinks the probability of flipping heads is 55%. How could we settle this?
We necessarily need a prior assumption, and I set the assumption to me being 99% sure I am right—coins are generally fair.
We then flip the coin 30 times, and end up with the following basket of evidence:
TTHTHHTTHTHTTHTHTTHHTTTTHTHHTT
(That is 18 heads and 12 tails, in that order)
We then need to ask ourselves two questions:
1- What are the chances we’d get that basket of evidence if I’m right?
2- What are the chances we’d get that basket of evidence if Kyler is right?
It turns out that if the probability of getting that string of results with a fair coin is 0.000000000931 (i.e. 1 in 1.07 billion). In contrast, the probability of getting that string of results with a coin weighted 55:45 towards heads is 0.000000001462 (i.e. 1 in 680 million). Knowing that, we can do some algebra and see that the results moved in Kyler’s direction. It isn’t overwhelming, but we can update the probability of the coin being weighted from 1% to 1.56%. At that point we could conduct some more experiments, gather more evidence, and update the probability further. Eventually we would converge on the correct answer.
[continued]
On the Interpreter blog, Dr. Kyler Rasmussen Ph.D. has begun a series of posts “summarizing and evaluating Book of Mormon-related evidence from a Bayesian statistical perspective.” While the moderators of the comment section of that website have been very generous allowing me to post my criticisms there, the formatting is subpar. I thought I’d post some comments regarding his methodology here.
To explain my point, I’m going to present a simple example of how Bayesian analysis is supposed to work that should be easy to understand. I’ll then use that as an analogy for what Dr. Rasmussen is doing wrong.
Simple Example
Say I have a coin. I have the hypothesis that the coin is fair, i.e. that the probability of flipping heads is 50%. In contrast, Kyler thinks the coin is weighted, and thinks the probability of flipping heads is 55%. How could we settle this?
We necessarily need a prior assumption, and I set the assumption to me being 99% sure I am right—coins are generally fair.
We then flip the coin 30 times, and end up with the following basket of evidence:
TTHTHHTTHTHTTHTHTTHHTTTTHTHHTT
(That is 18 heads and 12 tails, in that order)
We then need to ask ourselves two questions:
1- What are the chances we’d get that basket of evidence if I’m right?
2- What are the chances we’d get that basket of evidence if Kyler is right?
It turns out that if the probability of getting that string of results with a fair coin is 0.000000000931 (i.e. 1 in 1.07 billion). In contrast, the probability of getting that string of results with a coin weighted 55:45 towards heads is 0.000000001462 (i.e. 1 in 680 million). Knowing that, we can do some algebra and see that the results moved in Kyler’s direction. It isn’t overwhelming, but we can update the probability of the coin being weighted from 1% to 1.56%. At that point we could conduct some more experiments, gather more evidence, and update the probability further. Eventually we would converge on the correct answer.
[continued]