![]() The confidence interval does not provide a lot of practical value. In the non-Bayesian world, we may calculate confidence intervals. For example, we may flip a coin 100 times and calculate the number of heads to determine the probability of heads with the coin (if we believe it is a loaded coin). In the non-Bayesian (Frequentist) world, the parameter is assumed to be fixed, and we need to take many samples of data to make an inference regarding the parameter. ![]() In Bayesian Inference, we do not assume that the parameter (the value that we are calculating like Reliability) is fixed. There is the ubiquitous 90/90 or 95/95 confidence/reliability sample size table that is used for this purpose. Ideally, one would want to have some confidence that the widgets being produced is x% reliable, or in other words, it is x% probable that the widget would function as intended. ![]() In today’s post, I have attached a spreadsheet that calculates the reliability based on Bayesian Inference. ![]() ![]() One of the most common questions a statistician is asked is “how many samples do I need – is a sample size of 30 appropriate?” The appropriate answer to such a question is always – “it depends!” I have written about sample size calculations many times before. ![]()
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