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When conducting a clinical trial with small or sparse data sets, statistical methods meant for large sample sizes may fail to obtain an accurate interpretation of data. This is where computationally challenging exact methods often come into play.
Exact methods, however, are inferentially conservative in the sense that due to small sample sizes, the actual Type 1 error rate is often smaller than the nominal (intended) rate . There exists an array of strategies to combat this troublesome feature of exact tests, each of which varies along the parameter of computational complexity.