My question is if I’m trying to determine the sample size of migrated data to see if it migrated correctly to the target database, is the Z1.4 table applicable to that?
The scenario is data is being transferred from an old system to a new system and I want to do a quality check on the data in the new database to make sure everything was transferred correctly. I’m hoping to use the Z1.4 table to determine the sample size if its applicable. Is it applicable and if not, do you know of other standards that I should be looking into that is more applicable?
The movement of a database from one system to another certainly may introduce errors and it may also carry over errors that already exist. In some cases the move may also find and repair errors, yet that generally is done by design.
So, let’s say it’s just a move and you are checking for any new errors that are introduced.
Since you have access to the entire population, the database, in a before (old system) and after the move (new system) and I’m assuming you do not want to check every entry, instead just a sample, then I would recommend using an hypothesis test approach rather than a lot sampling approach.
A hypothesis test based on the binomial distribution may be appropriate as you are checking field entries to determine if they are correct or not (pass/fail).
You can set a threshold defect rate that you want to check the new system is at least this good or better, or you can measure the old system and compare to the new system – it should be equal to the old system as null hypothesis.
You can find a bit more information about a p-test in a good stats book or online at a short tutorial I wrote at https://creprep.wordpress.com/2013/06/01/hypothesis-tests-for-proportion/
The Z1.4 standard would require you to artificially define a lot or consider the entire database as one lot. The standard lot testing approach does not provide the control and statistical power of hypothesis testing, thus my recommendation. With the p-test you can define the confidence, defect rate to detect, and sample size to fit your needs concerning ability to make measurements, cost, and risk.