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Sample count affecting results: resolve with a user warning when sample counts < 100 are entered #177
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A sample count of 2 isn't a relevant use case. If you're seeing substantially different results (completely different research areas being funded) for sample counts of 100, 500, 1000, then we can look into this. But keep in mind this is stochastic optimization: the exact numerical results will differ every time it's run. |
I'm actually curious now if we can figure out what the minimum viable sample size should be. As the sample size increases, results should stabilize, so we may be able to pinpoint a good sample size that provides stable results without taking too long. I'm going to assign this issue to me and do a little exploration. Will post results when I have them (target end of December). |
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I'm not seeing anything in the above results to cause concern - even the difference between sample count = 2 and sample count = 1000+ is within rounding distance. @tjlca Did you see cause for concern in any other decision contexts? If not I'll close this issue. We might want to add guidance on choosing sample sizes to the documentation, to recommend only 100+ draws be used per simulation. ETA: Pressed the wrong button and closed accidentally! Re-opening until we're sure this isn't a problem. |
To address: add warning message for sample_count < 100 |
Different sample count numbers for developing tranche results affect the optimization results.
We tested for Sample_count = 2 and Sample_count = 100 and results were different.
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