Is there a sampling methodology / approach that focuses on selecting the fewest number of samples to cover the most amount of different attribute combinations in a population? I.e. optimizing sample selection to get a small but diverse sample (that's not necessarily representative of the overall population) that covers multiple attributes of interest.

Background: we need to manually pull and review records that forms our dataset, and we want to minimize the number of records we review.

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Without knowing more context it’s hard to say how you should proceed, but it’s pretty common to use various clustering/MiniSOM/PCA algos to identify which samples are similar and can be represented by a subset or trend

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I’m a statistician and may be able to help, you can DM me if you would like. One idea that came to mind was, if the population of interest can be represented as a categorical variable then you might consider using stratified random sampling to ensure that you have the highest diversity within the sample amid having a small sample size.

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Stratified random sampling

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