diff --git a/Comp/r-sas-wilcoxon-ranksum_hl.qmd b/Comp/r-sas-wilcoxon-ranksum_hl.qmd
index 2ce243f01..492490594 100644
--- a/Comp/r-sas-wilcoxon-ranksum_hl.qmd
+++ b/Comp/r-sas-wilcoxon-ranksum_hl.qmd
@@ -49,7 +49,7 @@ The following table provides an overview of the supported analyses between R and
 | Wilcoxon Rank-Sum -- Normal approximation **with** continuity correction | Yes | No | Yes | Yes | In {coin}, one can add `correct=TRUE`, but note that no error is given and the results of a normal approximation approach **without** continuity correction are provided.
 | Wilcoxon Rank-Sum -- Normal approximation **without** continuity correction | Yes | Yes | Yes | Yes |
 | Wilcoxon Rank-Sum -- Exact | Partly | Yes | Partly | Yes | In {stats}, one can only do the exact method when no ties are present.; In {asht}, exact test is possible but the run time is long for larger sample size.
-| Wilcoxon Rank-Sum -- Approximative (Monte Carlo simulation) | No | Yes | Yes | Yes |
+| Wilcoxon Rank-Sum -- Approximative (Monte Carlo simulation) | No | Yes | Yes | Yes | In SAS, "exact wilcoxon / mc n=100000"
 | Hodges-Lehmann estimator -- Asymptotic | Yes | No | No | Yes |
 | Hodges-Lehmann estimator -- Exact | Partly | Yes | No | Yes | In {stats}, one can only do the exact method when no ties are present.
 | Hodges-Lehmann estimator -- Approximative (Monte Carlo simulation) | No | Yes | No | No |
@@ -65,7 +65,7 @@ In the below table the p-values of the Wilcoxon Rank Sum Test with different opt
 | Wilcoxon Rank-Sum -- Normal approximation **with** continuity correction | 0.0100 | / | 0.0100 | 0.0100 | Yes | Not possible with {coin}
 | Wilcoxon Rank-Sum -- Normal approximation **without** continuity correction | 0.0094 | 0.0094 | 0.0094 | 0.0094 | Yes | 
 | Wilcoxon Rank-Sum -- Exact | / | 0.0082 | / | 0.0082 | Yes | Not possible with {stats} since there are ties.; In {asht} run-time very long. 
-| Wilcoxon Rank-Sum -- Approximative (Monte Carlo simulation) | / | 0.0083 | 0.0083 | / | Yes | With 100,000 simulations
+| Wilcoxon Rank-Sum -- Approximative (Monte Carlo simulation) | / | 0.0083 | 0.0083 | 0.0085 | Yes | With 100,000 simulations
 
 
 ### Hodges-Lehmann estimator