"Whether we and our politicians know it or not, Nature is party to all our deals and decisions, and she has more votes, a longer memory, and a sterner sense of justice than we do." — Wendell Berry

This is based upon my solution of Exercise 2.3, page 18, R. Christensen, W. Johnson, A. Branscum, T. E. Hanson, Bayesian Ideas and Data Analysis, Chapman & Hall, 2011. The purpose is to show how information latent in a set of data can be revealed by Bayesian inference or, as it is sometimes called in this case, Bayesian inversion.

There’s data about numbers of cars manufactured on different days, and data regarding numbers of those cars which are defective.

It would be interesting to know what’s the proportion overall that are defective. Well, it’s

Then, it’s interesting to ask, what’s the probability of a defective car given that it’s Friday, or

Now, this data is known. I buy a car. It’s defective. What are the odds it was manufactured on a Friday as opposed to a Tuesday?

My point? This needed be limited to problems regarding defective cars. Rather than calculating p-values from mysteriously posed t-tests applied to statistical populations for which the assumptions regarding t-tests are questionable, such as might arise in determining whether a temperature trend is statistically important, with a bit more sophistication, the odds of there being a trend versus their not being a trend can be calculated. Moreover, different models can be compared in similar ways.

See https://667-per-cm.net/about. Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study.

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Mike Bloomberg, 2020
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GeoEnergy Math
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