Goodness-of-fit tests are used to determine if the data at hand satisfy the distributional assumptions of the statistical model to be used. One of goodness-of-fit tests to use is the correlation coefficient test. This test is simple and easy to use, however it requires a special table derived from Monte Carlo simulations. This test is performed by ranking the data, associating with each datum the expected value of the order statistic with the same rank, computing the product-moment correlation coefficient between the data and the logistic deviates, and finally, using a table to find the probability of a good fit associated with the observed correlation. Calculated correlations that are smaller than the critical point indicate lack of fit. In this article, a table based upon simulations of size 100,000 for goodness-of-fit to the logistic distribution is presented. The sample sizes used in the current study ranged from 5 to 200 with increments of 5 for the sizes between 5 and 100, and increments of 10 for the sizes between 100 and 200. Each sample size is used with percentage points of .001, .01, .025, .05 .10, .25, .50, .75, and .95.