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I want to investigate the impact of various testing strategies on a product. Let's say chairs. I start with 500 random chairs that I've picked up from garage/yard sales. They come in all shapes and sizes, different manufacturers etc, but I've carefully measured each one and record: manufacturer, height, width, depth and fabric type. I calculate my population parameters. I find that some parameters are normal, and others are uniform, fabric types are mostly cotton but some leather.

I want to split my 500 chairs into groups of 100 such that each group has similar sample statistics. This way I can differentiate the impact of the various test on the chairs without worrying that I'm actually observing differences in my input distribution. I.e. I don't want all the leather chairs in one group.

I've tried randomly grouping my dataset, but I always end up with bad bias in one statistic. I thought that it might be possible to start with random grouping and then randomly selecting a pair of chairs to swap groups; recalculate group statistics; if they get closer to population parameter, keep the swap, else revert; repeat. That seems dreadfully slow.

I'm sure that there are many solutions available, but I'm not sure what they're called: what should I be searching for? If you have a solution handy, I don't really mind what language it is presented in. I'll add additional tags to help others find this sort of thing in the future. Thanks!

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