diff --git a/tutorials/model_testing_ppred_inf/scripts/PosteriorPredictive_TreeSummary.Rev b/tutorials/model_testing_ppred_inf/scripts/PosteriorPredictive_TreeSummary.Rev
index 6c09ab0bb..906499bf6 100644
--- a/tutorials/model_testing_ppred_inf/scripts/PosteriorPredictive_TreeSummary.Rev
+++ b/tutorials/model_testing_ppred_inf/scripts/PosteriorPredictive_TreeSummary.Rev
@@ -28,7 +28,7 @@ for ( i in 1:num_post_sims) {
     tree_length <- sim_tree_trace.computeTreeLengths()
 
     ## This calculates the entropy statistic
-    entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
+    entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)
     
     
     ## calculate the stuff we care about for a single pps posterior
@@ -73,7 +73,7 @@ rf_dists <- emp_tree_trace.computePairwiseRFDistances(credibleTreeSetSize=1.0,ve
 tree_length <- emp_tree_trace.computeTreeLengths()
 
 ## This calculates the entropy statistic
-entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
+entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)
 
 ## calculate the stuff we care about for a single posterior
 mean_rf = mean(rf_dists)
diff --git a/tutorials/model_testing_pps/pps_inference.md b/tutorials/model_testing_pps/pps_inference.md
index c05625ffb..ed9d7470e 100644
--- a/tutorials/model_testing_pps/pps_inference.md
+++ b/tutorials/model_testing_pps/pps_inference.md
@@ -449,7 +449,7 @@ Example topological test statistic calculations from a posterior distribution {%
 {% endfigure %}
 
 Since these values are calculated by iterating over the entire series of posterior predictive analyses
-they are a bit unweildy to run line by line. However, we will talk about some of the individual functions
+they are a bit unwieldy to run line by line. However, we will talk about some of the individual functions
 and how they work so if you wish you can develop your own test statistics at a later time.
 Here are some examples of these functions from the
 **PosteriorPredictive_TreeSummary.Rev** script:
@@ -469,7 +469,7 @@ Here are some examples of these functions from the
 > various topologies become increasingly uneven, entropy decreases causing the difference between the entropy of the prior and the 
 > posterior to increase. Equation $\ref{equation1}$.  
 
-```entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,numTaxa=data.ntaxa(),verbose=FALSE)```
+```entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)```
 
 Assuming a uniform prior on topologies, the change in entropy can be calculated as:
 
diff --git a/tutorials/model_testing_pps/scripts/PosteriorPredictive_TreeSummary.Rev b/tutorials/model_testing_pps/scripts/PosteriorPredictive_TreeSummary.Rev
index 6c09ab0bb..906499bf6 100644
--- a/tutorials/model_testing_pps/scripts/PosteriorPredictive_TreeSummary.Rev
+++ b/tutorials/model_testing_pps/scripts/PosteriorPredictive_TreeSummary.Rev
@@ -28,7 +28,7 @@ for ( i in 1:num_post_sims) {
     tree_length <- sim_tree_trace.computeTreeLengths()
 
     ## This calculates the entropy statistic
-    entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
+    entropy <- sim_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)
     
     
     ## calculate the stuff we care about for a single pps posterior
@@ -73,7 +73,7 @@ rf_dists <- emp_tree_trace.computePairwiseRFDistances(credibleTreeSetSize=1.0,ve
 tree_length <- emp_tree_trace.computeTreeLengths()
 
 ## This calculates the entropy statistic
-entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,num_taxa=data.ntaxa(),verbose=FALSE)
+entropy <- emp_tree_trace.computeEntropy(credibleTreeSetSize=1.0,verbose=FALSE)
 
 ## calculate the stuff we care about for a single posterior
 mean_rf = mean(rf_dists)