Quantification and metric optimization are powerful tools for reducing suffering, but they have to be used carefully. Many studies can be noisy, and results that seem counterintuitive may indeed be wrong because of sensitivity to experiment conditions, human error, measurement problems, or many other reasons. Sometimes you're looking at the wrong metric, and optimizing a metric blindly can be dangerous. Designing a robust set of metrics is actually a nontrivial undertaking that requires understanding the problem space, and sometimes it's more work than necessary. There can be a tendency to overemphasize statistics at the expense of insight and to use big samples when small ones would do. Finally, think twice about complex approaches that sound cool or impressive when you could instead use a dumb, simple solution.
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