Don't discount a "flyer" as fouled data unless you know you pulled the shot. It may have been a real variation.

Later, I took the ammo to the range and found it was not shooting quite the nice groups that I saw in the original work-up.

Nothing changed. Several times I had even left the bulletseating die in the press so I would be sure that the larger quantity I loaded would be seated just like the test shots. I questioned everything, but none of the usual suspects explained the anomalies.

It took me a long time to snap on the problem. The answer was so simple that it was one of those "bang-hand-on-forehead" moments: sample size.

Making Numbers Work
Statistics is the subdivision of mathematics that provides us tolls to understand numerical data. A big pile of numbers is pretty useless unless you can organize, manage, and study it in a way that helps you accomplish a task--make nuts and bolts, study an infectious disease, or develop reloading data.

I will be the first to admit that statistics has been abused more than a rented Jeep on a prairie dog hunt, often to twist some social data to one's political agenda. I'm not talking about that. I am going to look at how the simplest statistical principles can help you avoid the odd results I experienced.

Everything that gets built needs to conform to standards. If you're building lug nuts for the automotive industry, all those nuts have to fit standard shaft threads, and the outside must fit the hex socket of a wheel tool. With today's modern digital-inspection equipment, it's possible to test every nut for any different parameters and ship with the confidence that every lug nut in the load meets or exceeds specifications.

But let's change the product to something consumable like beer. Fancy digital equipment can tell you that each bottle is unflawed, filled to precisely the right level, and even that the beer's color and temperature are perfect. However, electronics can't tell you that this batch tastes right. That can't be done without someone taking a swig.

In the case of lug nuts, the testing is nondestructive. The beer testing requires some of the product be consumed in testing and is destructive. Ammo, whether you or a big ammo plant loads it, falls into the second category. You can check every round for overall length and visual defects, but even the latest laser-measuring and digital-inspection equipment cannot tell if the ammo meets velocity, pressure, or accuracy standards. For that, you have to pull some triggers and empty some cases.

With ammunition, destructive testing of every item means there is nothing left to ship or, for the hobbyist, nothing left to shoot. So how do we deal with this?

It Takes a Plan
The ammo manufacturer needs a plan for quality testing that destructively tests a sub-amount of a product batch yet gives confidence that the rest of the batch meets standards. Done right, the plan will leave plenty of product left to ship to market and keep stockholders happy. Here's where the power of pure statistics comes into play.

Sample size is a percentage of a batch, or lot, determined by the level of confidence required. The system used by most U.S. companies evolved from military quality standards. For every parameter specified, there is an acceptable quality level (AQL) that expresses the minimum level of a specific defect that can be tolerated. The AQL depends on the parameter, especially for ammunition. Detecting excessive pressure is more important to safety than detecting a cosmetic stain on a cartridge case. The AQL for pressure is set much higher to give the manufacturer a statistical confidence that the entire lot is safe. The AQL establishes how much of the product is consumed in testing.