In most cases, the exact distribution of the likelihood ratio corresponding to specific hypotheses is very difficult to determine. A convenient result, attributed to Samuel S. Wilks, says that as the sample size n approaches the test statistic has asymptotically distribution with degrees of freedom equal to the difference in dimensionality of and parameters the β coefficients as mentioned before on the omnibus test. e.g., if n is large enough and if the fitted model assuming the null hypothesis consist of 3 predictors and the saturated ( full ) model consist of 5 predictors, the Wilks' statistic is approximately distributed ( with 2 degrees of freedom). This means that we can retrieve the critical value C from the chi squared with 2 degrees of freedom under a specific significance level.