Exact Methods
Exact methods can be useful in situations where the asymptotic assumptions are not met. Standard asymptotic methods are based on the assumption that the test statistic follows a particular distribution when the sample size is sufficiently large. When the sample size is not large, asymptotic results may not be valid, with the asymptotic p-values differing perhaps substantially from the exact p-values. Asymptotic results may also be unreliable when the distribution of the data is sparse, skewed, or heavily tied.
SAS/STAT software provides exact computations for the following analysis:
The FREQ and NPAR1WAY procedures compute exact p values using fast and efficient network algorithms. These algorithms provide a substantial advantage over direct enumeration, which can be very time-consuming and feasible only for small problems. These procedures also can estimate exact p-values by Monte Carlo simulation. This can be useful for problems that are so large that exact computations require a great amount of time and memory, but for which asymptotic approximations may not be sufficient.
Inference in exact logistic regression is based on enumerating the exact distributions of sufficient statistics for parameters of interest in the model, conditional on the remaining parameters. Hirji, Mehta, and Patel (1987) developed an efficient algorithm for generating the required conditional distributions, thus making these methods computationally available. This technique is available in the LOGISTIC procedure.
You request exact analysis in each of these procedures with EXACT statements. The POINT option in the EXACT statement requests exact point probabilities for the test statistics. Further details are available in the SAS/STAT documentation.
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