Using Statistical Analysis to Correctly Assess Your Load Testing Results

You spend a lot of time developing loads, but, at the end of the testing process, do you short-change yourself by not analyzing your results sufficiently and drawing the correct conclusions? Many times reloaders are lead on fruitless wild goose chases by assigning too much importance to small sets of data.

Of course, if the results of two tests (i.e. loads) differs tremendously that tells something. But “tremendously” really depends on the sample size. The smaller that sample size is, the less confidence you can place in the results, so where do you draw the line and say two sets of results differ enough to say they are statistically significant (i.e. not due to chance variability)?

Of course, the more data you obtain, the more reliable your conclusions will tend to be, but it is not always feasible to gather more than 10 pieces of data (i.e. shots through a chronograph) per test, so we have to get the most we can out of the data we have (while at the same time not drawing unfounded conclusions).

This video tackles this problem by showing how I extract the most reliable conclusions possible (for a layman anyway) using a load test I recently conducted. The formulae using Standard Deviation values are easy to use, along with a chart of T-Critical values you can use in conjunction with your calculated answers.

The T-Critical chart will then tell you if there is a statistically significant difference between the two things you are testing.

This is an excerpt from my much longer “Standard Powder Measure Tested Against Hand-Weighed Loads – Is There A Difference?” video.

For another excerpt from the full-length video showing how best to calibrate a powder measure quickly, refer to:
For an earlier video showing the ballistics formula section of my reloading notes, refer to:
For an earlier video showing how to import data into an Excel spreadsheet and produce graphs, refer to:

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