Bayesian hypothesis testing for means

Hypothesis testing is similar, in principle, to what we have done previously; only now, we are using the marginal distribution of the mean from the posterior distribution. We compute the probability that the mean lies in the region corresponding to the hypothesis being true.

So, now, you want to test whether the true mean is less than 1,000 Ω. To do this, we get the parameters of the posterior distribution, and then feed these to the pnig_mu_marg() function:

We end up with a probability that is almost 1. It is all but certain that the resistors are not properly calibrated.