I got some good knowledge from the hornady episodes but to me the thing they completely ignored was they didn’t mention or look at average point of impact (Accuracy) all, they only really looked at extreme spread or mean radius, which are important but it only measures precision.
If you find a load that is accurate and precise then your are golden.
Yes. Miles, in one statement, indicates a better stat would be mean radius but didn’t follow up.
The Hornady podcast on sample size was wrong on many levels. It was a display of
individuals not familiar with correct statistical inference. A random variable (or random
experiment) is a test in the sample space and maps the outcome to a number on the real line. So a single result of one experiment (5-shoot group) would generate one outcome on the real number line, say 0.5 MOA. A 50-shot ES group is one experiment and generates one number (observation) for example 0.75 MOA. IT IS NOT A LARGE SAMPLE — IT IS A SAMPLE SIZE EQUAL TO ONE. If you wanted to get statistically valid results, you would aim for a sample size of 30 or so. That would be thirty 50-shot groups and then you could generate meaningful test statistics like confidence intervals, t-tests, standard errors, etc.
If you have ever listened to Bryan Litz talk about his shot groups and test stats, you will notice that he talks about sample sizes of say 30 where he obtains thirty 5-shot groups, for example. This is correct sampling technique. He is then able to generate standard errors, confidence intervals and other meaningful test stats based on a sample size of 30. With thirty 5-shot groups you generate 30 observations.
If you were to take the 50 shots and then use the random variable of mapping each shot to a
mean radius then you could generate a large sample of 50 and produce usable test statistics for population inference.
However, Hornady chose not to do this. They shot one group and generated one number and falsely called this a large sample.
Also, the Hornady analysis ignores all prior information. But that’s a deeper topic and gets into Bayesian stats.
