diff --git a/notebooks/34_analytical_approx.ipynb b/notebooks/34_analytical_approx.ipynb
index a114a888..a305c4eb 100644
--- a/notebooks/34_analytical_approx.ipynb
+++ b/notebooks/34_analytical_approx.ipynb
@@ -2049,223 +2049,10 @@
     "### Bootstrap estimate of the standard error\n",
     "\n",
     "Another way to obtain the standard error of the mean\n",
-    "(the standard deviation sampling distribution of the mean)\n",
-    "is to use the bootstrap approach.\n",
+    "(the standard deviation of the sampling distribution of the mean)\n",
+    "is to use the bootstrap estimate.\n",
     "\n",
-    "\n",
-    "TODO: import formulas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b85af6aa-078a-4a02-9f0c-a71871d0985a",
-   "metadata": {},
-   "source": [
-    "#### Example 1BT: test for the mean of Batch 04"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "65005726-e0a2-421c-aada-27842968d6eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kombucha = pd.read_csv(\"../datasets/kombucha.csv\")\n",
-    "ksample04 = kombucha[kombucha[\"batch\"]==4][\"volume\"]\n",
-    "n04 = len(ksample04)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "ed62b815-5faf-458e-9849-85cb09a76892",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.2252842390160474"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# bootstrap estimate for standard error of the mean\n",
-    "from stats_helpers import gen_boot_dist\n",
-    "kbars_boot04 = gen_boot_dist(ksample04, estfunc=mean)\n",
-    "sehat_boot04 = std(kbars_boot04)\n",
-    "sehat_boot04"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "3769ca83-069b-407f-8712-4f3b44fc8fbe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.128661805915673"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# compute the t statistic using bootstrap se\n",
-    "obst04bt = (obsmean04 - muK0) / sehat_boot04\n",
-    "obst04bt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "5316bae0-aae8-4b97-8602-68fe70e7a248",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.012147863549560177"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scipy.stats import t as tdist\n",
-    "rvT04 = tdist(n04 - 1)\n",
-    "pvalue04bt = tailprobs(rvT, obst04bt, alt=\"two-sided\")\n",
-    "pvalue04bt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78df6c04-514c-4515-9ef8-5700de0861e9",
-   "metadata": {},
-   "source": [
-    "The $p$-value is very small,\n",
-    "so our decision is to reject $H_0$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "275cff66-0f71-46ed-ac3f-4448936397f0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "660fa68f-0140-4d7d-91f9-47466f4c881c",
-   "metadata": {},
-   "source": [
-    "#### Example 2BT: test for the mean of Batch 01"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "90aa8990-0fa7-4e80-9206-d62c34a3990e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kombucha = pd.read_csv(\"../datasets/kombucha.csv\")\n",
-    "ksample01 = kombucha[kombucha[\"batch\"]==1][\"volume\"]\n",
-    "n01 = len(ksample01)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "178af06b-310a-43f1-94c1-daa0b9c5ed1f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.5501517384893604"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# bootstrap estimate for standard error of the mean\n",
-    "from stats_helpers import gen_boot_dist\n",
-    "kbars_boot01 = gen_boot_dist(ksample01, estfunc=mean)\n",
-    "sehat_boot01 = std(kbars_boot01)\n",
-    "sehat_boot01"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "c02d1766-5a31-4963-9603-62d545742e1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.5781692061148894"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# compute the t statistic using bootstrap se\n",
-    "obst01bt = (obsmean01 - muK0) / sehat_boot01\n",
-    "obst01bt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "0a858812-cd15-4b32-adfb-a3d7f4f1ed6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5664736858023267"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scipy.stats import t as tdist\n",
-    "\n",
-    "rvT01 = tdist(n01-1)\n",
-    "#######################################################\n",
-    "pvalue01bt = tailprobs(rvT01, obst01bt, alt=\"two-sided\")\n",
-    "pvalue01bt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50db8103-3aee-430e-bf2a-c1ca6ef1262d",
-   "metadata": {},
-   "source": [
-    "The $p$-value is very large,\n",
-    "so we have no reason to reject $H_0$."
+    "See problems...\n"
    ]
   },
   {
diff --git a/notebooks/35_two_sample_tests.ipynb b/notebooks/35_two_sample_tests.ipynb
index 914a3e25..81e87cde 100644
--- a/notebooks/35_two_sample_tests.ipynb
+++ b/notebooks/35_two_sample_tests.ipynb
@@ -2414,27 +2414,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "id": "1e0fc5ae-ae1a-4fdc-98af-5f25d8563e88",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "ttest_dmeans() got an unexpected keyword argument 'equal_var'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[54], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m scoresD \u001b[38;5;241m=\u001b[39m students[students[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurriculum\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdebate\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      3\u001b[0m scoresL \u001b[38;5;241m=\u001b[39m students[students[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurriculum\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlecture\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m----> 4\u001b[0m \u001b[43mttest_dmeans\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscoresD\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscoresL\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mequal_var\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mTypeError\u001b[0m: ttest_dmeans() got an unexpected keyword argument 'equal_var'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "students = pd.read_csv(\"../datasets/students.csv\")\n",
     "scoresD = students[students[\"curriculum\"]==\"debate\"][\"score\"]\n",
     "scoresL = students[students[\"curriculum\"]==\"lecture\"][\"score\"]\n",
-    "ttest_dmeans(scoresD, scoresL, equal_var=True)"
+    "# ttest_dmeans(scoresD, scoresL, equal_var=True)"
    ]
   },
   {
diff --git a/problems/chapter3_problems.ipynb b/problems/chapter3_problems.ipynb
new file mode 100644
index 00000000..79fa69e1
--- /dev/null
+++ b/problems/chapter3_problems.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e0b97d86-6fc4-4476-b96d-99d6724e56ee",
+   "metadata": {},
+   "source": [
+    "# Chapter 3 Problems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22116b0f-4475-4c69-a8fd-b51cee926deb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Notebook setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "354bc124-8e55-4ff7-9827-bd7012810e18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load Python modules\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1d924fc8-4dfe-428f-b6aa-9148741c185a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Useful colors\n",
+    "snspal = sns.color_palette()\n",
+    "blue, orange, purple = snspal[0], snspal[1], snspal[4]\n",
+    "# red = sns.color_palette(\"tab10\")[3]\n",
+    "\n",
+    "# High-resolution please\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "# Where to store figures\n",
+    "DESTDIR = \"figures/stats/intro_to_NHST\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8dfa6a70-c4a6-4cfe-9f2d-87b4d9d3def6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set random seed for repeatability\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13ee88bb-f7e6-47f0-967c-65316539c3a9",
+   "metadata": {},
+   "source": [
+    "$\\def\\stderr#1{\\mathbf{se}_{#1}}$\n",
+    "$\\def\\stderrhat#1{\\hat{\\mathbf{se}}_{#1}}$\n",
+    "$\\newcommand{\\Mean}{\\textbf{Mean}}$\n",
+    "$\\newcommand{\\Var}{\\textbf{Var}}$\n",
+    "$\\newcommand{\\Std}{\\textbf{Std}}$\n",
+    "$\\newcommand{\\Freq}{\\textbf{Freq}}$\n",
+    "$\\newcommand{\\RelFreq}{\\textbf{RelFreq}}$\n",
+    "$\\newcommand{\\DMeans}{\\textbf{DMeans}}$\n",
+    "$\\newcommand{\\Prop}{\\textbf{Prop}}$\n",
+    "$\\newcommand{\\DProps}{\\textbf{DProps}}$\n",
+    "\n",
+    "$$\n",
+    "\\newcommand{\\CI}[1]{\\textbf{CI}_{#1}}\n",
+    "\\newcommand{\\CIL}[1]{\\textbf{L}_{#1}}\n",
+    "\\newcommand{\\CIU}[1]{\\textbf{U}_{#1}}\n",
+    "\\newcommand{\\ci}[1]{\\textbf{ci}_{#1}}\n",
+    "\\newcommand{\\cil}[1]{\\textbf{l}_{#1}}\n",
+    "\\newcommand{\\ciu}[1]{\\textbf{u}_{#1}}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "(this cell contains the macro definitions like $\\stderr{\\overline{\\mathbf{x}}}$, $\\stderrhat{}$, $\\Mean$, ...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8e72c5a-819e-47ed-a01a-c507eccd6eec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81c8b0a0-7c1b-46f0-8867-42511be5609b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0a7f8ab-d03a-426d-a860-6bcda2b41258",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "086d8674-20c9-4263-9ea9-5f246012044f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce12fb85-d626-4eb5-8388-0e0654d44ed0",
+   "metadata": {},
+   "source": [
+    "#### Problem NN: alt t-test for the mean of Batch 04 (Example 1BT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "974407f2-0133-4082-9a04-89f71569d267",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "muK0 = 1000   # population mean (expected kombucha volume)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "74b4f817-c6a5-4287-9c06-81185c599a06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kombucha = pd.read_csv(\"../datasets/kombucha.csv\")\n",
+    "ksample04 = kombucha[kombucha[\"batch\"]==4][\"volume\"]\n",
+    "n04 = len(ksample04)\n",
+    "obsmean04 = np.mean(ksample04)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "da0edb55-02b2-401b-b82c-717d2d9ea40d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.225161704465105"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bootstrap estimate for standard error of the mean\n",
+    "from stats_helpers import gen_boot_dist\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "kbars_boot04 = gen_boot_dist(ksample04, estfunc=np.mean)\n",
+    "sehat_boot04 = np.std(kbars_boot04)\n",
+    "sehat_boot04"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1c9bcc1-d4d3-42ac-a65f-d9acbeff900d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1289747190340322"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute the t statistic using bootstrap se\n",
+    "obst04bt = (obsmean04 - muK0) / sehat_boot04\n",
+    "obst04bt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "44527229-74f4-4473-a83d-18aca57366be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.003314349648233716"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import t as tdist\n",
+    "from stats_helpers import tailprobs\n",
+    "rvT04 = tdist(n04 - 1)\n",
+    "pvalue04bt = tailprobs(rvT04, obst04bt, alt=\"two-sided\")\n",
+    "pvalue04bt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b237fe8d-4ffd-40d0-a348-2b8cfcd146db",
+   "metadata": {},
+   "source": [
+    "The $p$-value is very small,\n",
+    "so our decision is to reject $H_0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "821916cf-16dc-481c-be96-f35031bb1561",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c38c16b-9adc-4038-9256-aa7a79cf411a",
+   "metadata": {},
+   "source": [
+    "### Problem NN: alt t-test for the mean of Batch 01 (Example 2BT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "02d06cdb-777c-4cfd-8cda-f8ad61133805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "muK0 = 1000   # population mean (expected kombucha volume)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "48395f89-ed9a-4ca1-9fd9-ad96ce5e113b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kombucha = pd.read_csv(\"../datasets/kombucha.csv\")\n",
+    "ksample01 = kombucha[kombucha[\"batch\"]==1][\"volume\"]\n",
+    "n01 = len(ksample01)\n",
+    "obsmean01 = np.mean(ksample01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d884bf11-1d94-4b6f-bc0c-38423b290239",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.530831183342292"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bootstrap estimate for standard error of the mean\n",
+    "from stats_helpers import gen_boot_dist\n",
+    "np.random.seed(42)\n",
+    "kbars_boot01 = gen_boot_dist(ksample01, estfunc=np.mean)\n",
+    "sehat_boot01 = np.std(kbars_boot01)\n",
+    "sehat_boot01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9b753c28-dc9c-42c5-81ff-04c873f4198f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.5854662550335628"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compute the t statistic using bootstrap se\n",
+    "obst01bt = (obsmean01 - muK0) / sehat_boot01\n",
+    "obst01bt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fd48f6e0-a6a1-4ef1-919b-3a6e50027935",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5616069624592427"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import t as tdist\n",
+    "from stats_helpers import tailprobs\n",
+    "rvT01 = tdist(n01-1)\n",
+    "pvalue01bt = tailprobs(rvT01, obst01bt, alt=\"two-sided\")\n",
+    "pvalue01bt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb10344f-e830-4b05-912c-9130b7fd3f35",
+   "metadata": {},
+   "source": [
+    "The $p$-value is very large,\n",
+    "so we have no reason to reject $H_0$."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/problems/stats_helpers.py b/problems/stats_helpers.py
new file mode 120000
index 00000000..1315c255
--- /dev/null
+++ b/problems/stats_helpers.py
@@ -0,0 +1 @@
+../notebooks/stats_helpers.py
\ No newline at end of file