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I'd be interested in what sorts of results this exercise leads to. My understanding is that resistors are sold in three (?) different tolerance groups-1%, 5% 15% (?), but they are all manufactured by the same process. The finished resistors are then measured and those that meet the tolerance of the 1% group are put in one pile, those that meet the tolerance of the 5% group are put in another pile and all those that meet the 15% tolerance are put in a third pile. The rest are rejected. If this is true then one would seldom find a resistor in, say, the 15% category that is closer to the nominal value than 5%--hardly the expected Gaussian distribution. And in any event, there should be few if any whose tolerance is greater than 15%.
Actually, we do this as an introduction to uncertainty analysis and Student's t test. We have our students pick a sample of twenty nominal 100 ohm resistors from a larger set of 100. Measure the resistance of each. They mix-up the first sample then re-measure the resistance of each resistor. Then they measure the resistance of a second random sample of 20 from the set of 100 after throwing the first sample back in. Finally they measure the resistance of a sample of 20 different resistors with the same nominal value. We have reels of old (>30 years) resistors that on average are about 5% higher than the new ones.