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Have you seen my latest snapshot of shrapnel?



Travis

CZ? That's for the euro-trash that can't afford proper euro-trash.


Travis

I thought Sako was Euro trash???

It's proper euro-trash. Get your nomenclature straight.



Travis

Proper euro-trash says "Rigby's Special Big Bore for Big Game":




Sako trash:



Get it right fer Pete's sake.

BD

Dam you have a big gun BD.

If you get it hitting proper POI are you going to shoot a cow?

I don't shoot cows with that gun.

Funniest post I've seen in a while!

This REALLY needs to be accompanied by music. " Ode to Joy" springs to mind�.

My requirement is less than an inch at 100 yards off a bench. If it can't get under that at that range, I don't want it. My new custom, half inch or better at the range.

Reason for that being my personal limitations, I should only be concerned with my shooting and not the rifles. If the rifle is in question, it is fixed or gone. This new rifle is far better than I am and I don't have to worry about it at any range.

Monk's chanting..

That is really part of the question. Which is more significant. I've been using median thinking it would better reflect what I want to know by giving less emphasis to a single bad shot.

Consider these numbers: 2,2,2,3,100,100,100

Three is the median, but does it "best represent" the "middle" of these numbers?

3 best represents what to expect of the next shot more than 44 does. 3 doesn't represent well what the entire picture looks like.

Why? Those 100's are in there pretty strong.

And if we're doing something similar with the position of a group center, aren't we concerned with what the middle of the entire picture looks like?

The 43 in this example seems poor at predicting the future. Nothing was actually near or like the 43. That's the way I see it, but convince me I'm wrong.

I tested more than thirty loads from more than a dozen rifles this way. I compared the median, average, and group size with each other. The strongest correlation was between median and average as would be expected. The weakest was between median and total group size.

Mathman,

I'm no statistics expert, but I've seen enough data to be very suspicious of the data you listed. Looks like two different data sets somehow got combined, or there was some measurement error or some other problem. Bottom line is I wouldn't trust the data until I could figure out why 3 of the 7 data points were TWO orders of magnitude higher than the rest and the two "groups" of data within the data set were remarkably consistent in themselves but very different than those in the other "group." In fact, I would be wondering if the data represented a "zero" value at the low end (with some noise or some other phantom amount like nonlinearity at the bottom of a correlation curve showing instead of zero) and then values that were beyond the measurement capability of the system.

I know you were just throwing out some numbers for an example, but my point is blindly looking at a set of numbers is a trap that can lead to wrong conclusions. A lot more meaning can be derived from a data set when one knows something about the conditions under which the measurements were taken (e.g., gusty wind, a shot or two were pulled, etc.).

If I remember correctly, the mean and the median converge as the number of points in the data set increases. With a small number of data points, you have a strong possibility of running into "errors" with both the mean and median.

I'm not thinking of predicting the next shot as much as estimating the center of the distribution being sampled. So for a group I need the means in x and y directions. From the mean point I can them measure the radius to each individual shot. Once I have that number for each shot I can calculate mean radius, or root mean square radius.

The data was deliberately contrived to illustrate a problem with using the median.

It's not a problem with the median, any more than it is a problem with the mean to say that 44.14 represents the data set. The problem is in the nature of the data set, which is not homogeneous and independent. It is highly autocorrelated. It doesn't matter which descriptive statistic you use. You're screwed.

For homogeneous and independent data, it matters very little whether you use mean or median, unless you have outliers you want to more or less ignore. Median is frequently much easier to get when in the field.