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I have hesitated to post this, but there is some excellent information on statistics on the Precision Rifle Blog


Before posting this, I worked up some cases and his conclusions are valid. It is especially applicable to the Power Factor issue.
 

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I'M not a "max speed junky", either, but I'd think it difficult to develop an accurate load without knowing the amount of variance one has with a particular component combination. Additionally, if a certain threshold is not met or exceeded, accuracy becomes quite secondary quite rapidly. To use your colorful parlance "it don't matter how accurate it is if it don't make "Major". I'd certainly prefer a load that groups 6" at 50 yards but consistently registers 170 PF to one that exceeds the 165 PF threshold only 90% of the time and groups 2" at the same distance.
very true, I didnt take "making Major" into consideration..........
 

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Doom,

You'll also find a fair amount of statistics discussion and some other links in the Ballistics forum on this board.
 
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Doom,

You'll also find a fair amount of statistics discussion and some other links in the Ballistics forum on this board.
Agreed. The blog article I posted, unlike forum threads, is very straightforward and “easier” to follow. In particular I thought the discussion on SD confidence interval fit well with your post on the PF testing. I didn’t look to see if the blog link was posted elsewhere.
 

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The one you linked to goes to pains to be understandable by the non-mathematically inclined person and to explain why they should care, and does so successfully, so it is a good one.
 

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If we go back to the original question.... it's about getting an accurate velocity average, to meet the rules of a competition. So, part of that is not only the average, but what sorts of extreme spreads that different loads are giving. Lower extreme spread, less change of showing up for competition and not making the minimum threshold.

That's a situation where "more is better" for sure. If you want to get an idea of statistical validity of small sample sizes, look up Student's T-test.
I was going to comment regarding t-Statistics, but you beat me to it. People who know "a little" statistics believe that "2-sigma" includes a 95% confidence interval, but (as someone else posted) 30 shots are considered a minimum for the 2-sigma rule to equate to 95% confidence. The actual distribution is, as you say, the t-Distribution, which gradually approaches the standard normal distribution for a "large number of shots." What is "large?" Well, statisticians consider 30 shots to be "close enough." If you have N shots, the confidence coefficient can be found from "t-tables" for N-1 degrees of freedom (df). Instead of "2" (actually 1.96 for the standard normal distribution), this coefficient increases with decreasing N (reflecting less "knowledge" of the real population being sampled). For N=7 shots (df=6), the confidence coefficient (t_alpha) is 2.447, while for N=5 (df=4), the confidence coefficient is 2.776. To construct a 95% confidence interval, you calculate the mean (m) and sample standard deviation (s, using N-1 weighting), and then you can conclude with 95% confidence that the "true" mean is inside the interval m +/- s x t_alpha. With decreasing N, t_alpha increases, so you have a larger confidence interval.
 
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