I looked around for a suitably simple tutorial on sample estimation and confidence intervals so that those who are not familiar with the subject could understand it. I found a pretty good tutorial aimed at high school AP students:
Hopefully a "network engineer" should have enough math and logic background to follow it.
Now to take a specific example, let's look at the return rates for a given manufacturer, and say the sample size is 500 (the minimum allowed for inclusion in the BeHardware study). Also assume that the total population of parts produced by the manufacturer is much more than 500 (at least 10 times more) and that the sample had 10 returns for a return rate of 10/500 = 2%
Then just follow the recipe given in the link above. We assume it is a random sample (there is no reason to believe that the manufacturer cherry-picked parts to send to this etailer), there were at least 10 (or 5) failures. The population size is much larger than 500, so we can use the approximate formula for the standard error:
SE = sqrt( p * q / n ) = sqrt( 0.02 * 0.98 / 500 ) = 0.00626
It is convenient to go for a 95% confidence interval (since margin of error is equal to about 2 SEs, or more precisely 1.96 SE)
Then ME = 1.96 * 0.00626 = 0.0123
So the 95% confidence interval is 0.02 +/- 0.0123
Not an especially narrow interval, but that is why the BeHardware study chose 500 as the minimum sample size. Larger samples will have narrower intervals. Also, larger return rates will have intervals with widths proportionally smaller compared to the return rate.