  In the present paper, we develop a new goodness-of-fit test for the Birnbaum-
Saunders distribution based on the probability plot. We utilize the sample
correlation coefficient from the Birnbaum-Saunders probability plot as a
measure of goodness of fit. Unfortunately, it is impossible or extremely
difficult to obtain an explicit distribution of this sample correlation
coefficient. To address this challenge, we employ extensive Monte Carlo
simulations to obtain the empirical distribution of the sample correlation
coefficient from the Birnbaum-Saunders probability plot. This empirical
distribution allows us to determine the critical values alongside their
corresponding significance levels, thus facilitating the computation of the
p-value when the sample correlation coefficient is obtained. Finally, two
real-data examples are provided for illustrative purposes.

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