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I understand that to many this will be a frustratingly basic question which will not require much thought, but hopefully there are a few who can answer it anyway.
I have various sets of data on spreadsheets. Some worksheets have 900 rows of data and some have 15,000. There are many columns to each row, but essentially one row is one 'record'. The significant thing is that for each set of data, there is a finite and known population size.
I want to set up a 'checking' plan to provide me with a level of confidence (ideally without a tolerance, but if I have to have one then I have to have one). Each row (i.e. each record) on each worksheet can be correct or incorrect. There is no numerical variance, it is simply whether the data provided on each row accords to the various hard copy documents, data and calculations which feed into it.
My objective is to be able to state that I have reviewed the data and can say, with a given % confidence (and potentially tolerance, but ideally not), that the sample size I have examined (n) represents the larger population (N).
The ideal answer would provide me with a simple worked solution, but also a reference or several references to the genre of statistics that this exercise belongs in, so that I can go on to research it further and understand the basis for the worked solution provided. I understand that I may be looking at a huge sample size, but I want to investigate the various possibilities, for instance to be able to work the formula (optimistic that one exists) backwards to determine what % confidence I could have with an n sample size.
I have a spreadsheet with 1000 rows on it. In each row there is an invoice number and an invoice value, just two columns. I want to know whether all 1000 rows are correct, but I don't want to check all 1000 rows manually. How would I calculated a sample size for this (say 20, 50 or 75?) and what confidence level could I have that all 1000 were correct, if for example I checked 20 invoices and all 20 were correct.