diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb
index a7fdb028..1f09ba5c 100644
--- a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb
+++ b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb
@@ -1091,7 +1091,7 @@
     "\n",
     "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
     "\n",
-    "A chain that is [Isn't meandering exploring?] exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
+    "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
     "\n",
     "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC has a built-in autocorrelation plotting function in the `Matplot` module. "
    ]
diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb
index 37947ae9..6182a6c6 100644
--- a/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb
+++ b/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb
@@ -1073,7 +1073,7 @@
     "\n",
     "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
     "\n",
-    "A chain that is [Isn't meandering exploring?] exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
+    "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
     "\n",
     "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC3 has a built-in autocorrelation plotting function in the `plots` module. "
    ]
diff --git a/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb b/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb
index 05ce6a59..c1257def 100644
--- a/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb
+++ b/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb
@@ -1909,7 +1909,7 @@
     "\n",
     "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
     "\n",
-    "A chain that is [Isn't meandering exploring?] exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
+    "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
     "\n",
     "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. TFP has a built-in autocorrelation tools as well. "
    ]