The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples
Random assignment, typically seen as the standard in controlled trials, aims to make experimental groups statistically equivalent before treatment. However, with a small sample, which is a practical reality in many disciplines, randomized groups are often too dissimilar to be useful. We propose an a...
| প্রধান লেখক: | , , |
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| অন্যান্য লেখক: | |
| বিন্যাস: | প্রবন্ধ |
| ভাষা: | en_US |
| প্রকাশিত: |
Institute for Operations Research and the Management Sciences (INFORMS)
2015
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/1721.1/98509 https://orcid.org/0000-0003-1672-0507 https://orcid.org/0000-0002-1985-1003 |
| সংক্ষিপ্ত: | Random assignment, typically seen as the standard in controlled trials, aims to make experimental groups statistically equivalent before treatment. However, with a small sample, which is a practical reality in many disciplines, randomized groups are often too dissimilar to be useful. We propose an approach based on discrete linear optimization to create groups whose discrepancy in their means and variances is several orders of magnitude smaller than with randomization. We provide theoretical and computational evidence that groups created by optimization have exponentially lower discrepancy than those created by randomization and that this allows for more powerful statistical inference. |
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