Bearing surface vs. velocity
- Thread starter potatoe
- Start date
It’s a tough question but here is a parallel question. We all agree that powder weight affect MV, so the question is when we reload, how sensitive is our reloading practice in terms of detecting the MV variance when we intentionally vary the amount of powder charge in a case? I am sure for some, the variance as per their other variables associated with reloading is higher than the change in MV say that is caused by as much as 0.1 grain of powder. So if we cannot detect it, does it mean it does not matter? Or does it just mean that we have not controlled enough variables in your reloading practice to see it? I think the same thing applies to the variable you are asking about.
So the simple answer to your question is I don’t know and I don’t want to spent a lot of time and component to find out because if the effect is small (expected), it is going to be hard, time and component expensive. Basically the rule in statistics is the smaller the variable you are trying to see if it is significant, the larger the required sample size.
So I assume the worst and address it by sorting my bullets which is relatively easy… To answer your question, my first assumption is that they are not going to vary very significantly. I make this assumption by not buying cheap bullets and doing a quick distribution study by measuring 50 bullets. This 50 will give me a good idea of how much variance there is. I use a tackle box with 15 bins and taking the ES of weight variance from the 50 rounds and divide it by 12 with 2 extra backup for ones that goes outside of extrapolated variance. So if the ES from the 50 rounds is 12 thousands, then it is easy with 1 bin per 1 thousands variance. In my experience 12 thousands is pretty reasonable and so when you load up with the bullets, you are really only have 1-2 thousands variance with the bullets you are using (assuming you are not shooting hundreds of rounds), so that effect is minimal.
Of course if you don’t sort and the variance is unknown to you (i.e. you don’t know how good or bad your bullets are…) and it turns out to be 12 thousands or more, you do stand a change of loading up with bullets from the two extreme end of the spectrum in terms of bearing surface length and that I don’t think you want to do.
So the simple answer to your question is I don’t know and I don’t want to spent a lot of time and component to find out because if the effect is small (expected), it is going to be hard, time and component expensive. Basically the rule in statistics is the smaller the variable you are trying to see if it is significant, the larger the required sample size.
So I assume the worst and address it by sorting my bullets which is relatively easy… To answer your question, my first assumption is that they are not going to vary very significantly. I make this assumption by not buying cheap bullets and doing a quick distribution study by measuring 50 bullets. This 50 will give me a good idea of how much variance there is. I use a tackle box with 15 bins and taking the ES of weight variance from the 50 rounds and divide it by 12 with 2 extra backup for ones that goes outside of extrapolated variance. So if the ES from the 50 rounds is 12 thousands, then it is easy with 1 bin per 1 thousands variance. In my experience 12 thousands is pretty reasonable and so when you load up with the bullets, you are really only have 1-2 thousands variance with the bullets you are using (assuming you are not shooting hundreds of rounds), so that effect is minimal.
Of course if you don’t sort and the variance is unknown to you (i.e. you don’t know how good or bad your bullets are…) and it turns out to be 12 thousands or more, you do stand a change of loading up with bullets from the two extreme end of the spectrum in terms of bearing surface length and that I don’t think you want to do.
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