Strategies for Reducing the Sample Size — For when a sampling plan is used. Hello, Thanks for this clear sample plan. A sample size is 30 means 30 tests are required. Under certain circumstance if may be possible to test 
Strategies for Reducing the Sample Size — For when a sampling plan is used. Hello, Thanks for this clear sample plan. A sample size is 30 means 30 tests are required. Under certain circumstance if may be possible to test 3 units 10 times each.
One example is hardware like a glucose meter where 3 units can be tested 10 times each. It assumes the difference between the meters is small compared to the overall variation in the data.
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The key is repeated measures on the same unit vary as much as measurements on different units. An example of where it cannot be done is when the 10 measurements on one unit are close together but very different than the 10 measurements on the second and third units. They require as few as 15 samples in contrast to the minimum of above.
My tables of variables sampling plans range from samples. The 15 is due to the assumption of normality. Less than 15 samples does not provide enough data for verifying the normality assumption. While 15 is minimum, I would generally recommend 50 samples for mormaility test. It is sufficient to detect larger departures from normality which have the largest effect on the variable sampling plan. All these people really did was prove that the data was no too grossly non-normal to fail the test?
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Too large of number can lead to rejecting the normal distribution for small departures which have little effect on the sampling plan. I suggest, ideally You are correct that normality tests do not prove normality. Instead, they detect sizeable d