[FlyFish]

Identification and treatment of outliers is a hotly debated topic among statisticians and there is no universally accepted way of doing either. That said, it is possible to do better than simply looking at your chronograph data and throwing out numbers that are suspiciously high or low or, (much worse, and never valid regardless of how many measurements you have) simply deleting the highest and lowest numbers.

A reasonably valid way of doing so is to use the properties of the normal distribution (the so-called "bell-shaped curve"). I've tested a number of sets of chronograph data and all of them have approximated a normal distribution. Statistical theory tells us that approximately two-thirds of the measurements in a normal distribution will fall within plus or minus one standard deviation of the mean (average), about 95% will be within two SDs, and about 99% will be within 3 SDs. That is to say, only about one percent of the data will be 3 SDs or more away from the mean.

To determine if a particular measurement is a [likely] outlier, calculate how far away from the mean it is (subtract the average of the string from the measurement) and divide the result by the standard deviation - ignore any negative signs). This is pretty simple to do because the chronograph will give you the mean and SD. That result equals how many standard deviations away from the mean that particular data point is - this value is known as it's "z-score." If that number is 3 or greater, there is less than a 1% chance that the particular measurement comes from the same population as the others, and it can be considered an outlier. Note that you can't be 100% sure that it is an outlier, only that it's highly likely that it is.

Like any test, the more data points in the string the better the test will be. It's possible to correct for small sample sizes somewhat by using what's called a t-distribution (really a family of distributions) rather than the normal distribution, but that's probably more sophistication than is necessary for this application.