diff --git a/Chapter2_MorePyMC/MorePyMC.ipynb b/Chapter2_MorePyMC/MorePyMC.ipynb
index dc80dbad..d73edd4d 100644
--- a/Chapter2_MorePyMC/MorePyMC.ipynb
+++ b/Chapter2_MorePyMC/MorePyMC.ipynb
@@ -709,7 +709,7 @@
      "source": [
       "Had we had stronger beliefs, we could have expressed them in the prior above.\n",
       "\n",
-      "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if  $ X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1-p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data."
+      "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if  $ X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data."
      ]
     },
     {
@@ -1819,7 +1819,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "A Normal random variable can take on any real value, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n",
+      "A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n",
       "\n",
       "$$ E[ X | \\mu, \\tau] = \\mu$$\n",
       "\n",
@@ -2048,8 +2048,8 @@
      "input": [
       "from scipy.stats.mstats import mquantiles\n",
       "\n",
-      "# vectorized bottom and top 5% quantiles for \"confidence interval\"\n",
-      "qs = mquantiles(p_t, [0.05, 0.95], axis=0)\n",
+      "# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n",
+      "qs = mquantiles(p_t, [0.025, 0.975], axis=0)\n",
       "plt.fill_between(t[:, 0], *qs, alpha=0.7,\n",
       "                 color=\"#7A68A6\")\n",
       "\n",