diff --git a/11_Hypothesis_testing/11b_Statistical_modeling_with_Python 3.pptx b/11_Hypothesis_testing/11b_Statistical_modeling_with_Python 3.pptx
new file mode 100644
index 0000000000000000000000000000000000000000..548776d0ce263cd995f97ccdc41cf928d5b6c912
Binary files /dev/null and b/11_Hypothesis_testing/11b_Statistical_modeling_with_Python 3.pptx differ
diff --git a/11_Hypothesis_testing/Advanced python.ipynb b/11_Hypothesis_testing/Advanced python.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ac711b6354e3c70ebd9d25b9e2aa7780bfde2d26
--- /dev/null
+++ b/11_Hypothesis_testing/Advanced python.ipynb	
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Phytonic loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = [1, 3, 4, 7]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Classical loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 6, 8, 14]\n"
+     ]
+    }
+   ],
+   "source": [
+    "output1 = []\n",
+    "\n",
+    "for value in input:\n",
+    "    result = value * 2\n",
+    "    output1 = output1 + [result]\n",
+    "    \n",
+    "print(output1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pythonic loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 6, 8, 14]\n"
+     ]
+    }
+   ],
+   "source": [
+    "output2 = [value * 2 for value in input]\n",
+    "\n",
+    "print(output2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pythonic loops 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_image(filename):\n",
+    "    print(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Classical loop going through a folder processing all tif images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "banana0002.tif\n",
+      "banana0003.tif\n",
+      "banana0004.tif\n",
+      "banana0005.tif\n",
+      "banana0006.tif\n",
+      "banana0007.tif\n",
+      "banana0008.tif\n",
+      "banana0009.tif\n",
+      "banana0010.tif\n",
+      "banana0011.tif\n",
+      "banana0012.tif\n",
+      "banana0013.tif\n",
+      "banana0014.tif\n",
+      "banana0015.tif\n",
+      "banana0016.tif\n",
+      "banana0017.tif\n",
+      "banana0018.tif\n",
+      "banana0019.tif\n",
+      "banana0020.tif\n",
+      "banana0021.tif\n",
+      "banana0022.tif\n",
+      "banana0023.tif\n",
+      "banana0024.tif\n",
+      "banana0025.tif\n",
+      "banana0026.tif\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# define the location of the folder to go through\n",
+    "directory = 'C:/structure/teaching/lecture_applied_bioimage_analysis_2020/03_Feature_extraction_and_ImageJ_Macro/example_images/banana/'\n",
+    "\n",
+    "# get a list of files in that folder\n",
+    "file_list = os.listdir(directory)\n",
+    "\n",
+    "# go through all files in the folder\n",
+    "for filename in file_list:\n",
+    "    # if the filename is of a tif-image, print it out\n",
+    "    if filename.endswith(\".tif\"):\n",
+    "        process_image(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pythonic loop going through a folder processing all tif images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "banana0002.tif\n",
+      "banana0003.tif\n",
+      "banana0004.tif\n",
+      "banana0005.tif\n",
+      "banana0006.tif\n",
+      "banana0007.tif\n",
+      "banana0008.tif\n",
+      "banana0009.tif\n",
+      "banana0010.tif\n",
+      "banana0011.tif\n",
+      "banana0012.tif\n",
+      "banana0013.tif\n",
+      "banana0014.tif\n",
+      "banana0015.tif\n",
+      "banana0016.tif\n",
+      "banana0017.tif\n",
+      "banana0018.tif\n",
+      "banana0019.tif\n",
+      "banana0020.tif\n",
+      "banana0021.tif\n",
+      "banana0022.tif\n",
+      "banana0023.tif\n",
+      "banana0024.tif\n",
+      "banana0025.tif\n",
+      "banana0026.tif\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# define the location of the folder to go through\n",
+    "directory = 'C:/structure/teaching/lecture_applied_bioimage_analysis_2020/03_Feature_extraction_and_ImageJ_Macro/example_images/banana/'\n",
+    "\n",
+    "[process_image(filename) \n",
+    " for filename in os.listdir(directory)\n",
+    " if filename.endswith(\".tif\")\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Method parameters documented"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skimage.io import imread\n",
+    "from skimage import filters\n",
+    "from skimage import measure\n",
+    "\n",
+    "def analyse_image(filename, gaussian_blur_sigma, threshold=None):\n",
+    "    \"\"\"\n",
+    "    This function analyses an image by blurring it and applying a threshold.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    filename\n",
+    "        Image file to open and analyse\n",
+    "    \n",
+    "    gaussian_blur_sigma\n",
+    "        Sigma of a Gaussian blur filter kernel to be \n",
+    "        applied to the image before thresholding\n",
+    "    \n",
+    "    threshold\n",
+    "        Optional, the grey-value threshold to \n",
+    "        differentiate foreground and background\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    count\n",
+    "        Number of objects in the image\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # load image\n",
+    "    image = imread(filename);    \n",
+    "\n",
+    "    # Gaussian blur\n",
+    "    gaussian_blurred_image = filters.gaussian(image, 5)\n",
+    "\n",
+    "    # thresholding\n",
+    "    if threshold is None:\n",
+    "        threshold = filters.threshold_otsu(gaussian_blurred_image)\n",
+    "    thresholded_image = gaussian_blurred_image >= threshold\n",
+    "\n",
+    "    # run connected components analysis\n",
+    "    label_image = measure.label(thresholded_image)\n",
+    "    \n",
+    "    # analyse objects\n",
+    "    table = measure.regionprops_table(label_image)\n",
+    "    \n",
+    "    return len(table[\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "54\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analyse_image(\"blobs.tif\", 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "54\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analyse_image(\"blobs.tif\", gaussian_blur_sigma=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    This function analyses an image by blurring it and applying a threshold.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    filename\n",
+      "        Image file to open and analyse\n",
+      "    \n",
+      "    gaussian_blur_sigma\n",
+      "        Sigma of a Gaussian blur filter kernel to be \n",
+      "        applied to the image before thresholding\n",
+      "    \n",
+      "    threshold\n",
+      "        Optional, the grey-value threshold to \n",
+      "        differentiate foreground and background\n",
+      "\n",
+      "    Returns\n",
+      "    -------\n",
+      "    count\n",
+      "        Number of objects in the image\n",
+      "    \n",
+      "    \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analyse_image.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Information hiding, encapsulation\n",
+    "In classic functional programming, you hand over parameters and calculations are performed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "def area_of_ellipse(minor_axis, major_axis):\n",
+    "    return math.pi * minor_axis * major_axis / 4\n",
+    "        \n",
+    "def aspect_ratio_of_ellipse(minor_axis, major_axis):\n",
+    "    return major_axis / minor_axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area: 6.283185307179586\n",
+      "Aspect ratio: 2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "minor_axis = 2;\n",
+    "major_axis = 4;\n",
+    "\n",
+    "print(\"Area: \"         + str(area_of_ellipse(minor_axis, major_axis)))\n",
+    "print(\"Aspect ratio: \" + str(aspect_ratio_of_ellipse(minor_axis, major_axis)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python classes help to combine variables (e.g. minor and major axis) of objects (e.g. an ellipse)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "class Ellipse:\n",
+    "    \n",
+    "    def __init__(self, minor_axis, major_axis):\n",
+    "        if (minor_axis < major_axis):\n",
+    "            self.minor_axis = minor_axis\n",
+    "            self.major_axis = major_axis\n",
+    "        else:\n",
+    "            self.major_axis = minor_axis\n",
+    "            self.minor_axis = major_axis\n",
+    "            \n",
+    "    def area(self):\n",
+    "        return math.pi * self.minor_axis * self.major_axis / 4\n",
+    "        \n",
+    "    def aspect_ratio(self):\n",
+    "        return self.major_axis / self.minor_axis;\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You enter values once and afterwards, you can ask the object for various properties derived from these values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area: 6.283185307179586\n",
+      "Aspect ratio: 2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "e1 = Ellipse(4, 2)\n",
+    "\n",
+    "print(\"Area: \"         + str(e1.area()))\n",
+    "print(\"Aspect ratio: \" + str(e1.aspect_ratio()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/Equivalence testing.ipynb b/11_Hypothesis_testing/Equivalence testing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6bf3998f77539734a00747825c59b23d7e2f5cb6
--- /dev/null
+++ b/11_Hypothesis_testing/Equivalence testing.ipynb	
@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Equivalence testing\n",
+    "null-hypothesis: differences are out of a given range\n",
+    "alternative hypothesis: differences are small; and thus, bot samples are \"equal\" up to a given tolerance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import random\n",
+    "\n",
+    "x1 = random.normal(loc=5, scale=1, size=1000)\n",
+    "x2 = random.normal(loc=5, scale=1, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(x1, x2, \".\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.00030345809258036736,\n",
+       " (3.433984168873193, 0.00030345809258036736, 1998.0),\n",
+       " (-5.311451764509381, 6.043239585972563e-08, 1998.0))"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.stats.weightstats import ttost_ind\n",
+    "\n",
+    "pval = ttost_ind(x1, x2, low=-0.2, upp=0.2)\n",
+    "\n",
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Opposite situation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy import random\n",
+    "\n",
+    "x1 = random.normal(loc=6, scale=1, size=1000)\n",
+    "x2 = random.normal(loc=5, scale=1, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(x1, x2, \".\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.0,\n",
+       " (26.801197639278193, 9.755590019896484e-136, 1998.0),\n",
+       " (17.710726885754962, 1.0, 1998.0))"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.stats.weightstats import ttost_ind\n",
+    "\n",
+    "pval = ttost_ind(x1, x2, low=-0.2, upp=0.2)\n",
+    "\n",
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/T-Test.ipynb b/11_Hypothesis_testing/T-Test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..84608b90156e2c421f90eec1cf088db9e77a5ba5
--- /dev/null
+++ b/11_Hypothesis_testing/T-Test.ipynb
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T-Test\n",
+    "See also: https://en.wikipedia.org/wiki/Student%27s_t-test\n",
+    "\n",
+    "null-hypothesis: Our sample set has a mean that is equal to a given population mean.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "given_mean = 7 # this number serves for generating the sample\n",
+    "               # in a realistic scenario, we don't know it\n",
+    "\n",
+    "# generate random numbers following a normal distribution\n",
+    "x = random.normal(loc=7, scale=1, size=100)\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(x, bins=100)\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_xlabel(\"Day the patient felt better\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(x, bins=10, density=True)\n",
+    "ax.set_ylabel(\"likelihood\")\n",
+    "ax.set_xlabel(\"Day the patient felt better\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of the sample is 6.91035435725224\n",
+      "Standard deviation of the sample is 0.9794186535937979\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Descriptive statistics\n",
+    "mean = np.mean(x)\n",
+    "print(\"Mean of the sample is \" + str(mean)) \n",
+    "\n",
+    "standard_deviation = np.std(x)\n",
+    "print(\"Standard deviation of the sample is \" + str(standard_deviation)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value of or mean being 9: p = 2.1331173491593363e-37\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.1331173491593363e-37"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value of or mean being 4.0: p = 1.2080635908265571e-57\n",
+      "p-value of or mean being 4.5: p = 1.4774685646480428e-50\n",
+      "p-value of or mean being 5.0: p = 2.927549110228349e-42\n",
+      "p-value of or mean being 5.5: p = 2.0723760293438062e-32\n",
+      "p-value of or mean being 6.0: p = 8.80211621737463e-21\n",
+      "p-value of or mean being 6.5: p = 2.1235971268731145e-08\n",
+      "p-value of or mean being 7.0: p = 0.7671296791633029\n",
+      "p-value of or mean being 7.5: p = 2.97710641011874e-07\n",
+      "p-value of or mean being 8.0: p = 1.6669010569660399e-19\n",
+      "p-value of or mean being 8.5: p = 2.6682967439642807e-31\n",
+      "p-value of or mean being 9.0: p = 2.501526909114919e-41\n",
+      "p-value of or mean being 9.5: p = 9.090047576810763e-50\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.2080635908265571e-57, 1.4774685646480428e-50, 2.927549110228349e-42, 2.0723760293438062e-32, 8.80211621737463e-21, 2.1235971268731145e-08, 0.7671296791633029, 2.97710641011874e-07, 1.6669010569660399e-19, 2.6682967439642807e-31, 2.501526909114919e-41, 9.090047576810763e-50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The p-value tells us something about the probability that the mean of our distribution is exactly a given value. However, it's not the probabiltiy of the mean being exactly this value, because the p-value has something to do with the given value AND with the given distribution:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A different sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we change our sample-set distribution: still the same mean, but less samples\n",
+    "x = random.normal(loc=7, scale=2, size=10)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(x, bins=100)\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_xlabel(\"x\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of the sample is 8.119529116048758\n",
+      "Standard deviation of the sample is 2.0204845095615167\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Descriptive statistics\n",
+    "mean = np.mean(x)\n",
+    "print(\"Mean of the sample is \" + str(mean)) \n",
+    "\n",
+    "standard_deviation = np.std(x)\n",
+    "print(\"Standard deviation of the sample is \" + str(standard_deviation)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value of or mean being 5.0: p = 0.0012334275352099738\n",
+      "p-value of or mean being 5.25: p = 0.002109113565393285\n",
+      "p-value of or mean being 5.5: p = 0.0036779985289670967\n",
+      "p-value of or mean being 5.75: p = 0.006533286207939051\n",
+      "p-value of or mean being 6.0: p = 0.011793208822144456\n",
+      "p-value of or mean being 6.25: p = 0.021546001246203775\n",
+      "p-value of or mean being 6.5: p = 0.039593910627630714\n",
+      "p-value of or mean being 6.75: p = 0.07252704549826716\n",
+      "p-value of or mean being 7.0: p = 0.13082302205953084\n",
+      "p-value of or mean being 7.25: p = 0.2288536626571357\n",
+      "p-value of or mean being 7.5: p = 0.38163334982616415\n",
+      "p-value of or mean being 7.75: p = 0.5965754653989805\n",
+      "p-value of or mean being 8.0: p = 0.8630646511353139\n",
+      "p-value of or mean being 8.25: p = 0.8506951082698209\n",
+      "p-value of or mean being 8.5: p = 0.5859313467823023\n",
+      "p-value of or mean being 8.75: p = 0.3736492963758953\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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hn6o28egbu7j03BEJ//nxniy+sdftLmAncNOApxGRwHLO8djKXcwoLaSitMjvOCklNzuTT1aO47/+uJ36plZKCvMS+vPjPUdwR6+vv3LO/ZNz7qDX4UQkOFbtOMyWg8c1y6hHbpkz3rf5h075icDM7uMULSDn3BcGPJGIBNJjK3dRkJvFjRVafMYLvecf+vzVk8jJSty0Hf21htYmJIWIBNrBY22sqN3PkrkTyMvR4jNeuW1uGa/+9xpeqt3PRxK42tspC4Fz7uFEBRGR4HpqzR46ux23zNG8Ql66vNf8Q4ksBPFeUFZsZj8wsyoz+230y+twIuK/8OIze7j03OGcXXyW33FSml/zD8XbhHoc2ARMBL5FeNTQGo8yiUiA/O6dg+w92sptOkmcEH7MPxRvIRjunPsp0Omce805dydwsYe5RCQgHl25i1EFg7hm6ii/o6SF3vMPNbd1JuRnxlsIomnqzWyhmV0I6PpykRS361ALf9jSwOLZ47X4TAIlev6heP/PftfMCoG/Bf4OeAj4Un9PMrPrzWyzmW01s3tPsd+syER2N8eZR0QS4IlVu8kwY9EsnSROpETPPxRvIVjlnGtyzm1wzl3lnJvpnHv+VE8ws0zgfmA+MA1YbGbTTrLfvwIrPmB2EfFQdPGZa6eOYnShFp9JtETOPxRvIXjdzF42s0+b2dA4nzMb2Oqc2+6c6wCepO9pKT5PeMEbXaksEiAvbggvPnPbXJ0k9sPCihKGDc7h0Te8P2kc7xQTk4CvAecD68xsmZnd2s/TxgJ7et2vi2zrYWZjgY8CD5zqhczsLjNba2ZrGxoa4oksImfo0Td2cfYILT7jl+j8Q69sOkB9U6unPyvusz/OudXOuS8T/kv/MNDfxWZ9rVgR2+z6D+Ae51x3Pz/7QedcpXOusri4ON7IInKaNu5rZv3uo/zlnPFafMZHiZp/KN4LygrM7FNm9iLwOlBPuCCcSh0wrtf9UmBfzD6VwJNmthO4Gfixmf1FPJlExDuPrdpFbnYGn5g5rv+dxTO95x/q6Ap59nPi/UTwNnAB8G3n3HnOuXucc+v6ec4aYJKZTTSzHGAR8J4TzM65ic65Cc65CcDTwN9EFr0REZ8ciyw+c2OFFp8JgtvmltF4vJ2Xavd79jPiLQRnO+e+5Jx7w8xuiOcJzrku4G7Co4E2AU8552rNbKmZLT3NvCLisV+s3s2JDi0+ExS95x/ySlwL07j3DmT9NrAszudVAVUx2/o8Meycuz2e1xQR7zQeb+e+V7dyxXnFzBinxWeCIDr/0D9VbWJTfTNTSwoG/mecxnN05kgkRf37y5tp7ezm6ze875If8VF0/iGvThrHu2ZxLvA3wGXAETP7EvAT51ybJ6lEJOE27G3iyTV7+PSlEzl3pGYZDZKi/Bx++qlZVIwr9OT1412z+BHgGHBf5P5i4FHgE16EEpHEcs7xrRdqGZafw+fnTfI7jvThskneLWofbyGY7Jyb0ev+78zsbS8CiUjiLauuZ83OI3zvY+UU5mmkULqJ9xzBm2bWM+20mc0B/uRNJBFJpNaObr5XtYnzxxTwyUpdN5CO4v1EMAdYYmbRMxXjgU1mVkN4UFGFJ+lExHMPvLaNfU1t/MeiC8nM0FiQdBRvIbje0xQi4ou9R1t54LVt3FBRwuyJw/yOIz6J9zqCxK2ZJiIJ872qTQB8ZcFUn5OIn7TkkEiaWr3jMMuq61l6xTmMLcrzO474SIVAJA11hxz/+HwtYwpzWXrFOX7HEZ+pEIikoafW7mFjfTNfWTCVvJxMv+OIz1QIRNJMU2snP1ixmVkThnJDRYnfcSQA4h01JCIp4r5Xt3D4RAcP3zhbi84IoE8EImll68Hj/Pz1nSyaNY7pY72Zt0aSjwqBSBr57vKN5GVn8rcfnux3FAkQFQKRNPG7dw7y+80NfPGaSYw4a5DfcSRAVAhE0kBHV4jvLNvI2cWDWTJ3gt9xJGBUCETSwMOv72R7Ywtfv2EaOVn6Zy/vpd8IkRTXcKydH766hasmF3PV5JF+x5EAUiEQSXHR5Se/puUn5SRUCERS2Ia9Tfxy7R5uv2QC5xRr+UnpmwqBSIrqvfzkF67R8pNycioEIinqhcjyk39/3WQKcrX8pJycCoFICuq9/OQntPyk9EOFQCQFPfDaNuqb2vjmjedr+UnplwqBSIqJLj9544wxWn5S4qJCIJJi/rlqE2Zw7/wpfkeRJKFCIJJCVm0/xHItPykfkAqBSIroDjm+9cJGxhTm8teXa/lJiZ8KgUiKiC4/+dWFWn5SPhgVApEU0NTayfdXbGb2hGEsLNfyk/LBqBCIpIAfvrqFIyc6+MaN07T8pHxgKgQiSW7rweM8rOUn5QyoEIgksbbObu55ppq8nEz+TstPymnK8juAiJye7pDji0++yfrdR7hv8YUM1/KTcpr0iUAkCTnn+PpvNrCi9gDfuGEaN1SM8TuSJDEVApEk9J+vbuGJVbv57JXncMelE/2OI0nO00JgZteb2WYz22pm9/bx+C1mVh35et3MZniZRyQVPL5qF//xP1u4eWYp/3CdzgvImfOsEJhZJnA/MB+YBiw2s9i18nYAVzjnKoDvAA96lUckFayo3c/Xn9vAVZOL+d7HyjVUVAaEl58IZgNbnXPbnXMdwJPATb13cM697pw7Erm7Eij1MI9IUlu94zCf/8WbVJQWcf8tF5Gdqc6uDAwvf5PGAnt63a+LbDuZTwMv9vWAmd1lZmvNbG1DQ8MARhRJDpv3H+MzD6+hdGgeP7t9Fvk5GvAnA8fLQtDXZ1bX545mVxEuBPf09bhz7kHnXKVzrrK4uHgAI4oE396jrXzqZ6vJy8nkkTtnM2xwjt+RJMV4+WdFHdB7jbxSYF/sTmZWATwEzHfOHfIwj0jSOdLSwZKfrqKlo4tfLZ1L6dB8vyNJCvLyE8EaYJKZTTSzHGAR8HzvHcxsPPAscJtz7l0Ps4gkndaObu58eA17jrTy0JJKpowu8DuSpCjPPhE457rM7G5gBZAJ/Mw5V2tmSyOPPwB8AxgO/Dgy+qHLOVfpVSaRZNHVHeLuJ9bz9p6j/PiWi5hz9nC/I0kK8/SMk3OuCqiK2fZAr9ufAT7jZQaRZOOc46u/ruHVdw7y3b+YzvXTNa20eEvjz0QC5gcvb+aptXV8Yd4kbr24zO84kgZUCEQC5Od/2sH9v9vG4tnj+dI1k/yOI2lChUAkIJZV7+Nbyzby4Wmj+M5N5+uqYUkYFQKRAHh9WyNf/uXbzCobxg8XX0iWrhqWBNJvm4jPavc1cdcj65g4YjD/taSS3GwtPC+JpUIg4qM9h09w+3+voSA3i5/fOYvC/Gy/I0kaUiEQ8cmh4+0s+dlqOrtDPPLp2ZQU5vkdSdKUCoGID1rau7jz52uob2rlp5+axbkjh/gdSdKYpjAUSbCOrhCffXw9G/Y18+BtM5lZNtTvSJLm9IlAJIHaOrv5u1+9zR/ebeB7Hytn3tRRfkcS0ScCkURZs/Mw//B0NTsaW7jn+il8snJc/08SSQAVAhGPnejo4t9e2szDb+ykdGgeT3xmDpecO8LvWCI9VAhEPPT6tkbueaaaPYdbuf2SCfz9dZMZPEj/7CRY9Bsp4oFjbZ38y4vv8Piq3UwcMZin/nousycO8zuWSJ9UCEQG2GvvNvCVZ6rZ39zGX31oIl++djJ5ObpaWIJLhUBkgDS1dvLdZRv51bo6zh15Fk9/9hIuGq+hoRJ8KgQiA+DVTQf46q9raDzewd9ceQ5fmDdJcwZJ0lAhEDkDR1o6+NYLtTz31j6mjB7CQ0tmUV5a6HcskQ9EhUDkNL20oZ6vPVfL0RMdfHHeJD531bnkZOkaTUk+KgQiH1Dj8Xa++ZtaltfUc/6YAh65czbTxhT4HUvktKkQiMTJOccL1fX84/O1HG/r4u+vm8xdl59NthaRkSSnQiASh4PNbfyf5zbwysYDzBhXxPdvruC8UZoxVFKDCoHIKYRCjl+/uZdvL9tIW2c3X10whTsvnailJCWlqBCI9GFnYwvPrK/j2fV72Xu0lcqyofzbzRWcXXyW39FEBpwKgUhEc1sny6vreXpdHet2HSHD4LJJxdw7fwoLykvIzDC/I4p4QoVA0lp3yPHHLQ08s34vL9fup70rxKSRZ3Hv/Cl89MKxjCrI9TuiiOdUCCQtbTlwjKfX1/Hcm3s50NxOYV42/2vWOD5+USkVpYWY6a9/SR8qBJI2jrR08EL1Pp5ZV8fbdU1kZhhXTS7mH28s5eqpIxmUpSkhJD2pEEhK6+wO8drmBp5ZX8f/bDpAZ7djakkBX1s4lZsuGEvxkEF+RxTxnQqBpKSN+5p5Zn0dv3lrL43HOxg+OIfbLp7Ax2eO5fwxmgtIpDcVAkl6zjkONLfzdt1RauqaePWdg2yqbyY705g3ZRQ3zyzlisnFugJY5CRUCCTpNB5vp6auqeeNv3pvEw3H2gHIzDDKxxby7ZvO58aKMQwdnONzWpHgUyGQQDt6ooOavU1U1zVRHXnj39fUBoAZnFt8Fh+aNIIZpUWUlxYyraRA6wCIfEAqBBIYx9o62bC3mZq9R3m7romauiZ2Hz7R8/jEEYOpnDCMitJCKkqLOH9MgRaCFxkA+lckCdXe1c3B5nbqm9qob2qlvqmNzfuP8XbdUbY3tPTsVzo0j4rSQhbPHk9FaSHTxxRSmJ/tY3KR1KVCIAOmrbObA81t1De1sb+p7T1v9tH7jcfb3/e8UQWDqCgt4qMXjKW8tJDysYUMP0vDOkUSRYVATqmzO8Sxti6OtXVyrK2LptbOnjf7+qbWnjf4/U1tHGrpeN/zC3KzKCnMY3RhLtPHFjC6II+SwlxGF+b2fB+Sq7/0RfzkaSEws+uB/wQygYecc/8S87hFHl8AnABud86t9zJTunDO0dntaO3opjnyJn6srZPmXm/qve+Hv3fR3NrZ6/EuWju7T/ozivKzGV0QfkOfMa6IkoLoG3weJUW5jC7IVQ9fJAl49q/UzDKB+4FrgTpgjZk975zb2Gu3+cCkyNcc4CeR74EVCjlCztHtHM5BV8jR1R2KfHd0dofoDjm6Qn/eFrtPVyj05+29b3eH6OwO0d4V/mrr7A7fjnzvud8Vor2rm7bO8Pf2zj62dYVwrv//nkFZGRTkZTMkN4shudkU5GYxpiiXgtw/b+v9WEFeNqMKwm/yeTkanSOSCrz8c202sNU5tx3AzJ4EbgJ6F4KbgEeccw5YaWZFZlbinKsf6DC/33yQ7yzbiHPQ7cJv5qEQ4Tf1yJt7yPHn26HIfedwPfsMdKpTMwu/UedmZzIoK4NBWZnkZoe/h7dnUJSXzaD3bIvum8Gg7ExyszMZkptFQc+befSNPXxfi62LiJeFYCywp9f9Ot7/135f+4wF3lMIzOwu4C6A8ePHn1aYIbnZTBldQEaGkWGQaYaZkZkBGWbv2R6+bWRmGBbd1sc+ZpCdkUFmhpGdaWRmZJCVaWRlGFmZGeHvGUZ2Znif8GO99unjdnZmRs+bfXamaRZMEfGcl4Wgr3ew2L+p49kH59yDwIMAlZWVp/V3+cyyocwsG3o6TxURSWle9gXqgHG97pcC+05jHxER8ZCXhWANMMnMJppZDrAIeD5mn+eBJRZ2MdDkxfkBERE5Oc9aQ865LjO7G1hBePjoz5xztWa2NPL4A0AV4aGjWwkPH73DqzwiItI3Twd5O+eqCL/Z9972QK/bDviclxlEROTUNHZQRCTNqRCIiKQ5FQIRkTSnQiAikubMxTMhTYCYWQOw6zSfPgJoHMA4XlDGMxf0fBD8jEHPB8HPGLR8Zc654r4eSLpCcCbMbK1zrtLvHKeijGcu6Pkg+BmDng+CnzHo+XpTa0hEJM2pEIiIpLl0KwQP+h0gDsp45oKeD4KfMej5IPgZg56vR1qdIxARkfdLt08EIiISQ4VARCTNpWQhMLOdZlZjZm+Z2do+Hjcz+6GZbTWzajO7KIAZrzSzpsjjb5nZNxKcr8jMnjazd8xsk5nNjXk8CMewv4x+H8PJvX72W2bWbGb/O2Yf345jnPl8PYaRDF8ys1oz22BmvzCz3JjHff1djCOf78ewXy6yJm8qfQE7gRGneHwB8CLhFdIuBmJchMkAAAOMSURBVFYFMOOVwDIfj+HDwGcit3OAogAew/4y+noMY7JkAvsJX9QTqOPYTz6/fw/HAjuAvMj9p4Dbg3IM48wXmN/Dk32l5CeCONwEPOLCVgJFZlbid6igMLMC4HLgpwDOuQ7n3NGY3Xw9hnFmDJJ5wDbnXOxV8UH5XTxZviDIAvLMLAvI5/2rGPp9DPvLF3ipWggc8LKZrYssfB9rLLCn1/26yLZE6i8jwFwze9vMXjSz8xOY7WygAfhvM3vTzB4ys8Ex+/h9DOPJCP4dw1iLgF/0sd3v4xh1snzg4zF0zu0FfgDsBuoJr2L4csxuvh3DOPNBcH4P+5SqheBS59xFwHzgc2Z2eczj1sdzEj2Otr+M6wl/TJ8B3Ac8l8BsWcBFwE+ccxcCLcC9Mfv4fQzjyejnMexh4aVaPwL8qq+H+9iW0N/FfvL5egzNbCjhv/gnAmOAwWZ2a+xufTw1IccwznyB+D08lZQsBM65fZHvB4FfA7NjdqkDxvW6X0qCP871l9E51+ycOx65XQVkm9mIBMWrA+qcc6si958m/KYbu4+fx7DfjD4fw97mA+udcwf6eMzv4winyBeAY3gNsMM51+Cc6wSeBS6J2cfPY9hvvgAcw36lXCEws8FmNiR6G/gwsCFmt+eBJZHRBhcT/jhXH6SMZjbazCxyezbh/1eHEpHPObcf2GNmkyOb5gEbY3bz9RjGk9HPYxhjMSdvu/h6HCNOmi8Ax3A3cLGZ5UdyzAM2xezj5zHsN18AjmG/PF2z2CejgF9HjnsW8IRz7iUzWwo9ayZXER5psBU4AdwRwIw3A581sy6gFVjkIkMQEuTzwOORtsF24I6AHcN4Mvp9DDGzfOBa4K97bQvMcYwjn6/H0Dm3ysyeJtxe6QLeBB4MyjGMM5/vv4f90RQTIiJpLuVaQyIi8sGoEIiIpDkVAhGRNKdCICKS5lQIRETSnAqBiEiaUyEQEUlzKgQiZ8jMZkXmwc+NXDVea2bT/c4lEi9dUCYyAMzsu0AukEd4DqTv+RxJJG4qBCIDIDLNxRqgDbjEOdftcySRuKk1JDIwhgFnAUMIfzIQSRr6RCAyAMzseeBJwvPSlzjn7vY5kkjcUnH2UZGEMrMlQJdz7gkzywReN7OrnXO/9TubSDz0iUBEJM3pHIGISJpTIRARSXMqBCIiaU6FQEQkzakQiIikORUCEZE0p0IgIpLm/j9yCkMlgV5evwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.0012334275352099738, 0.002109113565393285, 0.0036779985289670967, 0.006533286207939051, 0.011793208822144456, 0.021546001246203775, 0.039593910627630714, 0.07252704549826716, 0.13082302205953084, 0.2288536626571357, 0.38163334982616415, 0.5965754653989805, 0.8630646511353139, 0.8506951082698209, 0.5859313467823023, 0.3736492963758953]\n"
+     ]
+    }
+   ],
+   "source": [
+    "range = np.arange(5, 9, 0.25)\n",
+    "results = []\n",
+    "for value in range:\n",
+    "    results = results + [test_if_mean_is(value)]\n",
+    "    \n",
+    "    \n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(range, results)\n",
+    "ax.set_ylabel(\"p-value\")\n",
+    "ax.set_xlabel(\"x\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(results)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value of or mean being 6.0: p = 0.023914983809892813\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "can only concatenate list (not \"NoneType\") to list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-21-edd57d4b05b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtest_if_mean_is\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"NoneType\") to list"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "null-hypothesis: there is no effect, distributions are equal\n",
+    "alternative hypothesis: mean is greater in sample2 compared sample1\n",
+    "\n",
+    "set threshold: significance level (alpha): How different does it have to be in order to be \"significantly different\": 1%, 5%, 100%\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/Testing curation time.ipynb b/11_Hypothesis_testing/Testing curation time.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..de404da5b12f81f2abb17d5efbe6257ff1afe81f
--- /dev/null
+++ b/11_Hypothesis_testing/Testing curation time.ipynb	
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setting up a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 7.27011443  9.85465087  6.95141023  9.24500459  8.90495633  5.79813565\n",
+      "  9.9992094   4.28286738  5.0511571   6.01641775  7.83666669  8.28679017\n",
+      "  8.34411823  8.40852419  6.08060269  7.51628825  5.25725523  8.04858072\n",
+      "  8.15182518  8.12191271  6.55381928  3.33632874  6.31461858  4.6032308\n",
+      "  4.94019806 13.35032912  6.03333339  1.85193828  8.1009366   4.87296902\n",
+      "  6.66458631  5.30685262  6.8634381   6.76577551  6.91485281  8.45594048\n",
+      " 10.96326062  8.2848168   5.35423583  6.08847733  5.79986437 11.02288371\n",
+      "  6.2350441   5.7274503   7.28870892  4.80090545  8.14478415 10.16106687\n",
+      "  8.38779289  8.5303685   5.60679502  6.46821253  9.13004099 10.87366831\n",
+      "  8.78542611 10.33541741  3.51638379  6.02953238  5.65259087  4.61802449\n",
+      "  7.2299876   4.78301236  5.37869989  7.48787149  6.38289068  7.19154068\n",
+      "  9.27137051  5.54527698 10.65534957  5.08766764  7.02866932  9.05415527\n",
+      "  7.6600142   9.267061    8.19955612  6.33452432  9.4027188   9.7310974\n",
+      "  6.48210483  6.26928475  5.27401951  6.74515644  0.68122265  8.18065681\n",
+      "  9.15268339  7.74697703 10.28201112  7.71229436  7.63087416  7.09212867\n",
+      "  4.14195812  3.42643788  2.43797867  6.96902512  5.80804667  4.03700635\n",
+      "  4.8747575   4.7259411   5.56147663  7.57001294]\n",
+      "[ 5.13524566  8.1089771   7.57939463  6.47165697  5.19815039  8.55567731\n",
+      "  1.83529688  9.70428483  8.13301694  9.97211549  7.73281157  8.07545269\n",
+      "  6.40288602  9.04946821  8.48383013 10.20897295  6.94660508 11.64089259\n",
+      "  8.44294434 10.59686178  8.12008165 10.34050033  6.58178866  6.34215898\n",
+      "  8.12158417  5.21995735  3.42094155  6.25153858  6.80850307  6.38487336\n",
+      "  7.77958492  8.04584323  5.16018103  5.32055156  3.2750884   4.74092265\n",
+      "  8.47644018  8.38169422  6.54200936  4.66342594  7.3811708   5.96611443\n",
+      "  2.5391737   5.80949073  8.43909846  8.35925088  7.73026043  8.92385488\n",
+      "  4.42756307  5.43570972  4.37772115  7.20869488  8.61585258  6.95059967\n",
+      "  5.36276821  8.55683181  5.63832339  8.98418562  9.40877157  7.10719578\n",
+      "  6.81846318  6.30936051  1.59327067  6.51518219  6.64126646  6.94963272\n",
+      "  6.16910104  8.44546918  7.27876465  6.3617845   5.65081483  4.77328228\n",
+      "  5.8978814  12.09305192  9.00288095  7.58249317  7.93682524  7.95180752\n",
+      "  6.61845776  5.44036616  8.23451973  8.40993964  7.10424768  5.81649113\n",
+      "  9.93482548  4.05564067 11.28050143  8.55349791  7.61527436  7.59975485\n",
+      "  8.71477125  5.0134487   9.63238054  3.53756252  6.05610185  6.78353894\n",
+      "  8.28886174  8.29957261  5.26320487  8.89943049]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import patients\n",
+    "\n",
+    "number_of_patients = 100\n",
+    "\n",
+    "curation_time_treatment = patients.treatment_group(number_of_patients)\n",
+    "curation_time_placebo = patients.placebo_group(number_of_patients)\n",
+    "\n",
+    "print(curation_time_treatment)\n",
+    "print(curation_time_placebo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Descriptive statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def do_descriptive_statistics(x):\n",
+    "    mean_x = np.mean(x)\n",
+    "    standard_deviation_x = np.std(x)\n",
+    "\n",
+    "    print(\"Mean: \" + str(mean_x) + \" +- \" + str(standard_deviation_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Treatment group\n",
+      "Mean: 6.966549057422408 +- 2.138934068447365\n",
+      "Placebo group\n",
+      "Mean: 7.10598565233434 +- 1.998905769266872\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Treatment group\")\n",
+    "do_descriptive_statistics(curation_time_treatment)\n",
+    "\n",
+    "print(\"Placebo group\")\n",
+    "do_descriptive_statistics(curation_time_placebo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(curation_time_treatment, bins=10)\n",
+    "ax.set_title('Curation time of ' + str(len(curation_time_treatment)) + ' patients treated')\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_xlabel(\"Curation time / days\")\n",
+    "plt.show()\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(curation_time_placebo, bins=10)\n",
+    "ax.set_title('Curation time of ' + str(len(curation_time_placebo)) + ' patients receiving a placebo')\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_xlabel(\"Curation time / days\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from my_statistics_functions import draw_curation_time_histogram\n",
+    "\n",
+    "draw_curation_time_histogram(curation_time_treatment, \n",
+    "                             \"patients receiving a treatment\")\n",
+    "draw_curation_time_histogram(curation_time_placebo, \n",
+    "                             \"patients receiving a placebo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hypothesis testing\n",
+    "\n",
+    "* null-hypothesis: Patients receiving the treatment feel better earlier\n",
+    "\n",
+    "* alternate hypothesis: Patients receiving the placebo need longer to feel better\n",
+    "\n",
+    "## Two-sample T-test of independent samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value: 0.6360930181448069\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "\n",
+    "presumptive_ripe_time = 25\n",
+    "\n",
+    "statistics, p_value = stats.ttest_ind(curation_time_treatment, curation_time_placebo)\n",
+    "\n",
+    "print(\"p-value: \" + str(p_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/Testing tomato ripen time.ipynb b/11_Hypothesis_testing/Testing tomato ripen time.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0b6a7b28f57db44e4667205e512b9512866393ad
--- /dev/null
+++ b/11_Hypothesis_testing/Testing tomato ripen time.ipynb	
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setting up a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[25.36077192 21.99284607 24.66147459 24.13522414 25.19699394 27.1105588\n",
+      " 27.21711827 23.99201324 24.62837502 24.00316964 23.97265165 23.69150348\n",
+      " 23.87474102 24.24951924 22.70964348 27.44811164 23.14164549 23.69577962\n",
+      " 24.63399963 25.52432004 24.44467109 24.68202734 20.99456432 25.01167432\n",
+      " 25.31265913 25.98709214 17.59602636 28.80564209 24.74442957 23.09989612\n",
+      " 24.0190898  22.8590973  22.8516007  25.67153251 25.13355891 26.42321083\n",
+      " 27.99791641 20.84282366 21.66138292 23.38381399 20.58994995 26.24360199\n",
+      " 25.36720024 25.46465655 21.03491453 24.78498624 27.36868498 27.48801593\n",
+      " 25.57688679 25.54842762 26.74737965 22.51636024 25.75046789 20.93175349\n",
+      " 25.83472988 23.41791145 22.6338715  22.12807181 29.58382288 21.54881035\n",
+      " 24.60023214 25.09047661 24.75968111 24.64405223 25.7446681  25.201116\n",
+      " 23.38080789 22.85446879 20.55882516 24.80651406 25.26496377 25.95638214\n",
+      " 24.69654821 26.11271043 24.57061342 26.21995055 23.57329152 22.38873739\n",
+      " 23.79337091 25.38534656 24.89694605 22.29055239 23.23316104 26.95960368\n",
+      " 23.48341966 25.05951226 22.38369459 24.06997003 27.97668846 25.86545142\n",
+      " 26.85137435 25.39114133 23.21363012 24.38962764 22.08019012 26.99443634\n",
+      " 24.40998955 26.6712733  25.06958356 27.04129782]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tomatoes\n",
+    "\n",
+    "number_of_tomatoes = 100\n",
+    "\n",
+    "ripe_time = tomatoes.ripen(number_of_tomatoes)\n",
+    "\n",
+    "print(ripe_time)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Descriptive statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def do_descriptive_statistics(x):\n",
+    "    mean_x = np.mean(x)\n",
+    "    standard_deviation_x = np.std(x)\n",
+    "\n",
+    "    print(\"Mean: \" + str(mean_x) + \" +- \" + str(standard_deviation_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 24.512579751104184 +- 1.9787623359862967\n"
+     ]
+    }
+   ],
+   "source": [
+    "do_descriptive_statistics(ripe_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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E/Ax4qGn0wcC5efhc4JCq2jczs4F1+xzANhGxBCD/nNhuRkkzJPVJ6lu+fHnXCjQzK8WIPQkcEbMjYlpETOvp6am7HDOz9U63A2CppEkA+eeyLrdvZmZZtwPgcmB6Hp4OfL/L7ZuZWVblZaAXAtcDL5J0j6T3AicCb5Z0B/Dm/NzMzGpQ2c3gIuKwNpP2rqpNMzPr3Ig9CWxmZtVyAJiZFcoBYGZWKAeAmVmhHABmZoVyAJiZFcoBYGZWKAeAmVmhHABmZoVyAJiZFaqyW0FYeXpnXVl3CWY2CN4DMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrlAPAzKxQDgAzs0L5+wDWQ74vv62v6vrfXnziAbW0WzXvAZiZFcoBYGZWKAeAmVmhHABmZoWq5SSwpMXACmA1sCoiptVRh5lZyeq8CuiNEfFAje2bmRXNh4DMzApVVwAEcLWkeZJmtJpB0gxJfZL6li9f3uXyzMzWf3UFwB4RsRuwH/BBSW9oniEiZkfEtIiY1tPT0/0KzczWc7UEQETcl38uAy4Fdq+jDjOzknU9ACRtKmmz/mHgb4AF3a7DzKx0dVwFtA1wqaT+9r8VET+soQ4zs6J1PQAi4k7g5d1u18zM1uTLQM3MCuUAMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrlAPAzKxQDgAzs0I5AMzMCuUAMDMrVB3fCWxmNqr0zrqytrYXn3hAZcv2HoCZWaEcAGZmhXIAmJkVygFgZlYoB4CZWaEcAGZmhXIAmJkVar3/HMD6ev2umdm68h6AmVmhHABmZoVyAJiZFcoBYGZWqFoCQNK+km6X9HtJs+qowcysdF0PAEljgDOB/YCXAodJemm36zAzK10dewC7A7+PiDsj4ing28DBNdRhZla0Oj4HMAW4u+H5PcCrmmeSNAOYkZ+ulHR7xXVNAB4YzgXqpOFc2qAMe19q5L6MTOtLX0Z8PwaxHmnVl+cP9At1BIBajItnjYiYDcyuvpxEUl9ETOtWe1VyX0Ym92XkWV/6AUPrSx2HgO4Btmt4vi1wXw11mJkVrY4A+A2wk6QXSNoIOBS4vIY6zMyK1vVDQBGxStJRwI+AMcA5EbGw23W00LXDTV3gvoxM7svIs770A4bQF0U86/C7mZkVwJ8ENjMrlAPAzKxQRQaApHMkLZO0oGHcVEk3SJovqU/S7nXW2ClJ20m6VtIiSQslfSSP31rSXEl35J9b1V3rQAbox5cl3SbpFkmXStqy7lrXpl1fGqYfIykkTairxk4N1BdJH8q3dFko6eQ66+zEAP9jo+q9L2kTSb+WdHPux2fz+MG/5yOiuAfwBmA3YEHDuKuB/fLw/sBP666zw75MAnbLw5sBvyPdYuNkYFYePws4qe5ah9iPvwE2zONPGun9GKgv+fl2pAsg/gBMqLvWdfi7vBH4MbBxnjax7lrXoS+j6r1P+izV+Dw8FrgRePVQ3vNF7gFExM+Ah5pHA5vn4S0YJZ9NiIglEXFTHl4BLCJ92vpg4Nw827nAIfVU2Jl2/YiIqyNiVZ7tBtLnRka0Af4mAF8FPkaLDz+ORAP05QPAiRHxZJ62rL4qOzNAX0bVez+Slfnp2PwIhvCeLzIA2pgJfFnS3cApwCdqrmfQJPUCu5K2CLaJiCWQ/vGBifVVNjhN/Wj0D8APul3Pumjsi6SDgHsj4uZaixqipr/LC4HXS7pR0nWSXllnbYPV1JdR996XNEbSfGAZMDcihvSedwA84wPA0RGxHXA0cHbN9QyKpPHAxcDMiHis7nqGql0/JB0HrAIuqKu2wWrsC6n244BP11rUELX4u2wIbEU69PCvwEWSWt3mZcRp0ZdR996PiNURMZW0R7y7pJcNZTkOgGdMBy7Jw98l3bV0VJA0lvQPfUFE9PdhqaRJefok0pbCiNamH0iaDrwFODzyAc6RrkVfdgReANwsaTHpjXuTpOfVV2Vn2vxd7gEuyYcjfg08TboZ2YjWpi+j9r0fEY8APwX2ZQjveQfAM+4D9szDbwLuqLGWjuWtrrOBRRFxasOky0n/2OSf3+92bYPRrh+S9gU+DhwUEX+uq77BaNWXiLg1IiZGRG9E9JJWoLtFxP01lrpWA/x/XUZ6nyDphcBGjPS7arbvy6h670vq6b8aTtI4YB/gNobwni/yk8CSLgT2Im2xLAU+A9wOnEbatX0C+OeImFdXjZ2S9Drg58CtpK0wgGNJxzYvArYH/gj8fUQ0n/geMQbox+nAxsCDedwNEfH+7lfYuXZ9iYirGuZZDEyLiJG+0mz3d/kxcA4wFXgKOCYiflJLkR0aoC+PMYre+5J2IZ3kHUPaiL8oIj4n6bkM8j1fZACYmZkPAZmZFcsBYGZWKAeAmVmhHABmZoVyAJiZFcoBYCOepNX5To0LJF3RcA30ZEnfG6Y2Zkp6TsPzq6q686ikH0qaMsD0XjXcqdasKg4AGw0ej4ipEfEy0k38PggQEfdFxNuHqY2ZwP8HQETsnz9lOazyB3e2joh7h3vZZoPlALDR5nrynTUbt5QlHSnp+3nr+nZJn+n/BUlH5Punz5f0DUljGhco6cPAZOBaSdfmcYslTcht3CbpP/MeyAWS9pH0y3zf9d3z/Jsqfc/EbyT9j6SD29S/F+mj+2uQ9Ip8f/fryQHX0MefS7opP16bx5/f2Eau6yBJOzf09RZJOw3+JbZi1H1vaz/8WNsDWJl/jiHdq2Xf/LyX/J0OwJHAEuC5wDhgATANeAlwBTA2z/fvwLtbtLGYhvvz9z/PbawC/pq0wTSP9AlYkW6/e1me/4vAEXl4S9K95jdt0c7pwJtajL8F2DMPf7mhX88BNsnDOwF9eXjPhra3AO4ifZL1DNI9kyDdnmFc3X8/P0buY8OhxYZZV43Lt77tJa2A57aZb25EPAgg6RLgdaSV9yuA3+SbVY5j8DfGuysibs3LXQhcExEh6dZcE6QvrjlI0jH5+Sakj+QvalrWHsAxjSMkbQFsGRHX5VHnA/vl4bHA1yRNBVaTbsNMRFwn6UxJE4G3AhdHxKq8B3GcpG1JN2sb0fe1sXo5AGw0eDwipuYV5X+TDpGc3mK+5vuaBGlL/dyIWJd7vD/ZMPx0w/OneeY9JOBtEXF7u4VI2gG4OyKeap5E+y+IOZp0v6qXk/ZAnmiYdj5wOHAo6bsSiIhvSboROAD4kaR/jBF+jx6rj88B2KgREY8CHwaOybf1bfZmpe9FHUf6NqRfAtcAb89byv3fm/r8Fr+7gvQ1gUP1I+BD/ffEl7Rri3n2A37YPDLSyeZH883KIK3U+20BLImIp4F3kQ6D9ZtDOnlNRCzM7e4A3BkRp5PuDrnLOvTJ1nMOABtVIuJ/gJtJW73NfkHaKp5POiTSFxG/BT4JXC3pFtLho0ktfnc28IP+k8BDcALpcM0t+cT0CS3m2ZcWAZC9BzgzH8J5vGH8vwPTJd1AOvzzp/4JEbGUdIjpmw3zvxNYkA+ZvRg4b2jdsRL4bqC2XpB0JOn2ykfVXUsrkjYGfhkR04Zxmc8h3dp4t7x3ZDYo3gMw64KIeHKYV/79XwJyhlf+NlTeAzAzK5T3AMzMCuUAMDMrlAPAzKxQDgAzs0I5AMzMCvV/rwBefQcD7l8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(ripe_time, bins=10)\n",
+    "ax.set_title('Ripe time of ' + str(len(ripe_time)) + ' tomatoes')\n",
+    "ax.set_ylabel(\"count\")\n",
+    "ax.set_xlabel(\"Ripe time / days\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hypothesis testing\n",
+    "\n",
+    "* null-hypothesis: Tomatoes become ripe after 25 days\n",
+    "\n",
+    "* alternate hypothesis: Ripe time of our tomatoes is different from 25 days\n",
+    "\n",
+    "## Parametric test: One sample t-test\n",
+    "Parameter: Presumptive mean of the population: 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'ripe_time' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-40d3242832e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpresumptive_ripe_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mstatistics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mttest_1samp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mripe_time\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpresumptive_ripe_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"p-value: \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'ripe_time' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "\n",
+    "presumptive_ripe_time = 25\n",
+    "\n",
+    "statistics, p_value = stats.ttest_1samp(ripe_time, presumptive_ripe_time)\n",
+    "\n",
+    "print(\"p-value: \" + str(p_value))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/Using custom libraries.ipynb b/11_Hypothesis_testing/Using custom libraries.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c2ca91805e956affa6173fa668a27931f83a704e
--- /dev/null
+++ b/11_Hypothesis_testing/Using custom libraries.ipynb	
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math_library\n",
+    "\n",
+    "x = 3\n",
+    "y = 7\n",
+    "\n",
+    "result = math_library.add(x, y)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-4\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math_library import subtract\n",
+    "\n",
+    "\n",
+    "result = subtract(x, y)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/blobs.tif b/11_Hypothesis_testing/blobs.tif
new file mode 100644
index 0000000000000000000000000000000000000000..fd5be7ff4274d197af9732e7a11c64f8e2e3f46b
Binary files /dev/null and b/11_Hypothesis_testing/blobs.tif differ
diff --git a/11_Hypothesis_testing/distributions.ipynb b/11_Hypothesis_testing/distributions.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..18ba619c680b90c0a8f936d557e39a6270e75170
--- /dev/null
+++ b/11_Hypothesis_testing/distributions.ipynb
@@ -0,0 +1,1068 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import random\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Uniform distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "69.3477764428305\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get a single random number between 0 and 100\n",
+    "x = random.uniform(0, 100)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[56.84711013 55.34219327 67.14059109 58.00834898 94.01891963 23.84325919\n",
+      "  8.77782622 37.2162182  70.48293836 34.63782848]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get 10 random numbers\n",
+    "x = random.uniform(0, 100, size=10)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[31.7475544  23.77999003 59.3542624  ... 31.11347844 65.49780918\n",
+      " 76.55802087]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# improve readability by writing all parameter names\n",
+    "x = random.uniform(low=0, high=100, size=10000)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make a 2 dimensional distribution of random numbers and plot it\n",
+    "\n",
+    "x = random.uniform(low=0, high=100, size=100)\n",
+    "y = random.uniform(low=0, high=100, size=100)\n",
+    "\n",
+    "\n",
+    "plt.plot(x, y, \".\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gaussian / Normal distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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i9+a4ztwyJZN91ec4UtvgdBQTwqxAmLC1oaSatAFx5I0a6nSUkJM/1VM0N5RYN5PpnBUIE5aa21y8tu/ke3cPmw/KHZLEtJxBdh3CXFRQC4SI5IvIfhEpFZGH/ez/hIgUeh+bRWSGz74jIlIkIrtEpCCYOU34efNgLY2tLhZMte6lzuRPzWTX8TNUnW1yOooJUUErECISjWcZ0QXAZOBuEZnc4bDDwLWqOh34LrCyw/7rVXWmquYFK6cJT+uLqxiYEMO8salORwlZF4rnRjuLMJ0I5hnEHKBUVctUtRVYBSz1PUBVN6vqae/LrUBuEPOYCNHmcvPXPSe4cfIwYqOtF7UzY9IHcNmwAay3AmE6EczfnhzguM/rcu+2znwKWO/zWoG/iMgOEVneWSMRWS4iBSJSUFNTc0mBTXjYcugU9c3tLLC5l7qUPzWL7UfqqD3f4nQUE4ICKhAi8ryILBKR7hQUf1cG/d7bLyLX4ykQ3/DZPF9VZ+PponpQRK7x11ZVV6pqnqrmpaendyOeCVfri6tJiovmQ+PTnI4S8vKnZOJW2LTnhNNRTAgK9D/8XwIfBw6KyPdFZGIAbcqB4T6vc4HKjgeJyHTgCWCpqp66sF1VK73PJ4EX8XRZGXNRLreyaU8110/MICE22uk4IW9SVgojU5NsNJPxK6ACoap/VdVPALOBI8Am76ij+0UktpNm24HxIjJaROKAZcBq3wNEZATwAnCvqh7w2Z4sIikXvgZuBoq790czkajgSB2151vt5rgAiQj5UzLZfKiWs01tTscxISbgLiMRSQXuAz4NvAv8DE/B2OTveFVtBx4CNgJ7gWdVtUREVojICu9h3wZSgV90GM46DHhLRHYD24C1qrqhu384E3k2lFQTFxPF9RMznI7Sb+RPzaTNpfxtn3UzmQ+KCeQgEXkBmAg8DSxR1QuTuPzpYvcoqOo6YF2HbY/5fP1pPAWnY7syYEbH7cZcjKqysbiaa8anMSA+oB9tA8zIHUzmwATWF1Vz+ywbSGjeF+gZxBOqOllV//tCcRCReAC7R8GEisLys1SebSbfRi91S1SUkD81k9cP1NDY2u50HBNCAi0Q3/OzbUtvBjHmUq0vriYmSrhxknUvddctUzJpaXfz9/02VNy876Ln4SKSiefehUQRmcX7Q1cHAklBzmZMwFSVDcVVzBubyuCkOKfj9DtXjBrC0OQ41hdX2+JK5j1dddTegufCdC7wY5/t54B/C1ImY7pt/4lzHDnVyKc/NMbpKP1STHQUN08exiu7K2luc9kQYQN00cWkqk+q6vXAfap6vc/jVlV9oY8yGtOlDcXViMDNU4Y5HaXfumVqJg2tLt4urXU6igkRXXUx3aOqzwCjROQrHfer6o/9NDOmz20oriZv5BAyUhKcjtJvzR+bRkp8DBuKq/nwJCu0puuL1Mne5wFAip+HMY47XNvAvupzNnrpEsXFRPHhSRls2nuCNpfb6TgmBFz0DEJVH/c+/0ffxDGm+y5ME5Fvaz9csvypWby0q5Jth+uYP87msop0XXUxPXKx/ar6hd6NY0z3bSipZnruIHIGJzodpd+79rJ0EmOjWV9cZQXCdDmKaUefpDCmhyrPNLH7+Bm+nj/B6ShhITEumusmpLOx5AT/eetUomy51ojWVRfTk30VxJieeK97ySbn6zX5UzNZX1zNjmOnuWLUUKfjGAd11cX0U1X9koi8gp+1HFT11qAlMyYA64urmJiZwpj0AU5HCRs3TMwgLiaKdUVVViAiXFddTE97n38U7CDGdNeJ+mYKjp7myzde5nSUsJKSEMs149PZUFzNtxZNtm6mCNbVjXI7vM+v45l76TRQB2zxbjPGMRtLqlGFhdOse6m3LZyWSdXZZnaVn3E6inFQoEuOLgIOAY8A/wuUisiCYAYzpivriqoYnzGAcRl2S05v+/CkYcRGC+uLqro+2IStQGdz/X/A9ap6napeC1wP/CR4sYy5uJpzLWw7XMcCm1guKAYlxnL1uDTWFVWj6ncpeRMBAi0QJ1W11Od1GXCyq0Yiki8i+0WkVEQe9rP/EyJS6H1sFpEZgbY1ke0ve6pxW/dSUC2YlkXFmSaKKs46HcU45KIFQkQ+IiIfAUpEZJ2I3Cci/wS8gmfN6Yu1jQYeBRYAk4G7RWRyh8MOA9eq6nTgu8DKbrQ1EWxdURVj0pKZMMy6l4Ll5snDiIkS1hVVOx3FOKSrM4gl3kcCcAK4FrgOqAGGdNF2DlCqqmWq2gqsApb6HqCqm1X1tPflVjzTigfU1kSuU+db2FpWx4JpmYjYCJtgGZwUx7yxqawvrrJupgjV1Y1y91/Ce+cAx31elwNXXuT4TwHru9tWRJYDywFGjBjR06ymH9m05wQut9rCNn1g0bQsHn6hiD1V9UzJHuR0HNPHAh3FlCAiD4rIL0TkNxceXTXzs83vxxARuR5PgfhGd9uq6kpVzVPVvPT09C4imXCwrriakalJTM4a6HSUsHfzlEyio4T11s0UkQK9SP00kIlnhbnX8XQFneuiTTkw3Od1LlDZ8SARmQ48ASxV1VPdaWsiz5nGVjaX1pI/1bqX+sLQ5DjmjhnKuiLrZopEgRaIcar6LaDBOz/TImBaF222A+NFZLSIxAHLgNW+B4jICOAF4F5VPdCdtiYybSyppt2tLJ6W7XSUiLFwWhZltQ3srerqM6EJN4EWiDbv8xkRmQoMAkZdrIGqtgMPARuBvcCzqloiIitEZIX3sG8DqcAvRGSXiBRcrG3gfywTrl7ZXcXI1CSm5lj3Ul9ZMDWL6CjhlUI7iY80Xc3FdMFKERkCfAvPJ/kB3q8vSlXXAes6bHvM5+tPA58OtK2JbLXnW9h8qJbPXTfOupf60NDkOOaPS2NNYSVfv2WC/d1HkIDOIFT1CVU9raqvq+oYVc24sNqcMX1lfbHn5rjFM2z0Ul9bPD2L43VNFJbbTXORJNBRTKki8nMR2SkiO0TkpyKSGuxwxvhas7uScRkD7OY4B9wyOZPYaOGV3dbNFEkCvQaxCs/UGncAdwK1wJ+CFcqYjk7UN7PtSB2Lp2dZF4cDBiV5pgBfW1SF222jmSJFoAViqKp+V1UPex/fAwYHM5gxvtYWVqEKi6fb6CWnLJ6RRdXZZnYeO931wSYsBFogXhORZSIS5X18FFgbzGDG+FpTWMmkrIGMy7CV45xy46RhxMdEsabQpgCPFF1N1ndOROqBzwB/AFq9j1XAl4MfzxioONPEzmNnWDzdLk47KSUhlusnZLC2qAqXdTNFhK5WlEtR1YHe5yhVjfE+olTVBqKbPrHWO/5+iXUvOW7xjCxqzrXwzuFTXR9s+r1Au5gQkVtF5Efex+JghjLG15rCKqbnDmJEapLTUSLeDRMzSIyN5pXd1s0UCQId5vp94IvAHu/ji95txgRVWc15CsvP2tlDiEiKi+GmycNYX1xFa7vb6TgmyAI9g1gI3KSqv1HV3wD53m3GBNVLuyqJErh1phWIUHH7rBzONLbx+oEap6OYIAu4i4kPDmu1ieFN0KkqL71bwfxxaQwbmOB0HON19fg0UpPjeOndCqejmCALtED8X+BdEfmdiDwJ7PBuMyZodh47w7G6RpbOzHE6ivERGx3FkhnZbNp7gvrmtq4bmH6rywIhIlGAG5iLZ2ruF4B5qroqyNlMhHvp3QoSYqO4Zcowp6OYDm6blUNru5sNtpBQWOuyQKiqG3hIVatUdbWqvqyq9lNhgqq13c2awkpumpxJSkKs03FMBzNyBzE6LZkXrZsprAXaxbRJRL4qIsNFZOiFR1CTmYj2xoEaTje2cfssuzgdikSE22bmsPXwKSrPNDkdxwRJoAXiAeBzeJYbLfB5XJSI5IvIfhEpFZGH/eyfKCJbRKRFRL7aYd8RESnyXUjIRI4Xd1UwNDmOD423dcZD1W2zslGF1TbDa9gKtEBMBh4FdgO7gJ8DUy7WQESivW0WeNvfLSKTOxxWB3wB+FEnb3O9qs5U1bwAc5owUN/cxl/3nGDJ9Cxio7sz0M70pZGpycweMdhGM4WxQH/7ngQmAY/gKQ6TvNsuZg5Qqqplqnph/qalvgeo6klV3c77S5oaw4bialra3dw2y0YvhbrbZ+Wwr/oce6vqnY5igiDQAjFBVT+tqq95H8uBCV20yQGO+7wu924LlAJ/8S5QtLwb7Uw/9+LOCkalJjFzuM0oH+oWTc8mJkrsYnWYCrRAvCsicy+8EJErgbe7aONvVZfuTAE5X1Vn4+mielBErvH7TUSWi0iBiBTU1Nidnf3d8bpGtpSd4iOzc21hoH5gaHIc10/M4IWdFbS5bOqNcBNogbgS2Oy9cHwE2AJc672IXNhJm3JguM/rXCDgq1mqWul9Pgm8iKfLyt9xK1U1T1Xz0tPtgmZ/9+eC44jAnZfnOh3FBOhjecOpPd/Ca/tOOh3F9LKYAI/L78F7bwfGi8hooAJYBnw8kIYikgxEqeo579c3A//ZgwymH3G5led2lPOh8elkD050Oo4J0HUT0klPiefZgnJunpLpdBzTiwIqEKp6tLtvrKrtIvIQsBGIBn6jqiUissK7/zERycQzXHYg4BaRL+EZ8ZQGvOjtYogB/qCqG7qbwfQvb5XWUnm2mW8u6jjYzYSymOgo7pidy6/eLONkfTMZNm9W2Aj0DKJHVHUdsK7Dtsd8vq7G0/XUUT0wI5jZTOh5tuA4Q5JiuXFyhtNRTDfdlZfLY68f4oV3K1hx7Vin45heYoPMTUg43dDKppIT3DYrh/iYaKfjmG4amz6AK0YN4dmC46jacqThwgqECQkv7aqg1eXmo3nDuz7YhKS78oZTVtPAjqOnnY5ieokVCOM4VeVP248zPXcQk7JsqfP+atG0LJLjonm24HjXB5t+wQqEcVxxRT37qs9xl5099GvJ8TEsnp7NmsIqzre0Ox3H9AIrEMZxq7YfIz4miltn2Myt/d1HrxhOY6uLNTaBX1iwAmEcda65jZferWDR9CwGJdq6D/3d7BGDmTAshWfeOWoXq8OAFQjjqBd2VtDQ6uKT80Y5HcX0AhHh3nkjKa6o593jZ5yOYy6RFQjjGFXl6a1HmZE7yCbmCyO3z8ohJT6Gp7d0+/5aE2KsQBjHbDl0itKT57nXzh7CSnJ8DHdcnsvawipqz7c4HcdcAisQxjFPbTnKkKRYFk/PcjqK6WX3zB1Jq8vNn7bbkNf+zAqEcUTV2SY27T3BR68YTkKs3TkdbsZlDGD+uFT+8M4xXG67WN1fWYEwjvjDO8dwq3LPlSOdjmKC5N65o6g408Sre084HcX0kBUI0+da2938cdtxbpiQwfChSU7HMUFy46QMsgcl8PRWu1jdX1mBMH1ufbHn4uW98+zsIZzFREfx8StH8ObBWg7VnHc6jukBKxCmT6kqv3qzjDFpyVwz3lYADHfL5owgLiaKJ9487HQU0wNWIEyf2nLoFMUV9Sy/ZgxRUbbmdLhLGxDPnZfn8vzOcmrO2ZDX/iaoBUJE8kVkv4iUisjDfvZPFJEtItIiIl/tTlvTPz32RhnpKfHcNivH6Simj/zzh8bQ5nLz5OYjTkcx3RS0AiEi0cCjwAI8y4jeLSId15KsA74A/KgHbU0/s6eynjcO1HD//FE2tDWCjE5LJn9KJk9tOUKDzfLarwTzDGIOUKqqZaraCqwClvoeoKonVXU70Nbdtqb/WfnGIZLjovmEDW2NOMuvGUN9czur7Ma5fiWYBSIH8P1pKPdu69W2IrJcRApEpKCmpqZHQU3wlZ9u5JXCKu6eM8JmbY1As0YM4crRQ/n1m2W0udxOxzEBCmaB8HcFMtBbKgNuq6orVTVPVfPS021UTKj69VuHEeCBq0c7HcU4ZMW1Y6k828yaQlsror8IZoEoB3yXCMsFAv3JuJS2JsScbmhl1bbj3Dozm+zBiU7HMQ65bkI6E4al8PjrZbZWRD8RzAKxHRgvIqNFJA5YBqzug7YmxPz27cM0tbn4zDVjnY5iHCQiLL9mDPuqz7Fpj02/0R8ErUCoajvwELAR2As8q6olIrJCRFYAiEimiJQDXwH+XUTKRWRgZ22DldUET11DK79+6zCLpmUxITPF6TjGYUtnZjM6LZkfbzqA2ybxC2kHN/8AABFPSURBVHkxwXxzVV0HrOuw7TGfr6vxdB8F1Nb0P4+/foimNhdfvmm801FMCIiJjuJLN47ni6t2sa64isXTbR3yUGZ3UpugOVnfzJNbjnDbzBzGZdjZg/FYPD2by4YN4MebDtBuI5pCmhUIEzS/+Psh2lzKF2+0swfzvugo4Ss3XUZZTQMv77KxJ6HMCoQJisozTfzhnWPcdXkuI1OTnY5jQswtUzKZkj2Qn7160O6LCGFWIExQ/PxvpQB8/sN29mD+kYjw1ZsncKyuked2lDsdx3TCCoTpdYdrG/hzwXHunjOcHLvvwXTiugnpzB4xmEdePUhTq8vpOMYPKxCm131vzR7iY6J48IZxTkcxIUxE+Eb+RKrONvP4G4ecjmP8sAJhetXf95/k1X0n+cKHx5ORkuB0HBPirhyTyqLpWTz2+iEqzjQ5Hcd0YAXC9JrWdjf/uWYPo9OSuX++zblkAvNvCycB8H/X7XU4ienICoTpNU9uPkJZTQPfWjyJuBj70TKByRmcyGevHcfawiq2HDrldBzjw36LTa+oOdfCI68e5LoJ6dwwcZjTcUw/85lrx5AzOJH/eKXEbp4LIVYgTK/44cZ9NLe7+NZiW/jPdF9CbDTfXDSJfdXn+KMtKhQyrECYS/ZO2Sn+vKOc++ePZmz6AKfjmH5qwdRM5o1J5Ycb9nGivtnpOAYrEOYSNba287XnChk+JIkv2ZQa5hKICP91+1Ra2t382wtFtmZECLACYS7J/2zYz7G6Rv7nzukkxQV1cmATAcakD+Dr+RN5dd9JXthZ4XSciGcFwvTY1rJT/G7zEe67ahRzx6Q6HceEifuvGsUVo4bwH6+UUH3WupqcZAXC9EhDSztfe243I1OT+Hr+BKfjmDASFSX88M4ZtLrc/OsLhdbV5KCgFggRyReR/SJSKiIP+9kvIvKId3+hiMz22XdERIpEZJeIFAQzp+m+H2zYR/npJn545wzrWjK9blRaMl+/ZSKv7a/hzzaZn2OCViBEJBp4FFgATAbuFpGOYyAXAOO9j+XALzvsv15VZ6pqXrBymu5bV1TFU1uOcv9Vo5kzeqjTcUyYuu+qUVw5eijfebmEAyfOOR0nIgXzDGIOUKqqZaraCqwClnY4ZinwlHpsBQaLSFYQM5lLdKjmPF/7825mDh/MNxZY15IJnqgo4ed3zyI5PoYVT+/gXHOb05EiTjALRA7ge8dLuXdboMco8BcR2SEiyzv7JiKyXEQKRKSgpqamF2KbzjS0tLPi6R3Ex0bzi0/MJj4m2ulIJsxlDEzg0Y/P4mhdI1/7s12P6GvBLBDiZ1vHf92LHTNfVWfj6YZ6UESu8fdNVHWlquapal56enrP05qLUlUefqGIQzXn+fnds8i2dR5MH7lyTCoP509kQ0k1T7x52Ok4ESWYBaIcGO7zOhfouABtp8eo6oXnk8CLeLqsjEN++/YRXtldyb/cPIH549KcjmMizKc/NJoFUzP5/oZ9bD5U63SciBHMArEdGC8io0UkDlgGrO5wzGrgk97RTHOBs6paJSLJIpICICLJwM1AcRCzmotYV1TFd9fu4abJw/jstWOdjmMikIjwP3dOZ3RaMp95egd7q+qdjhQRglYgVLUdeAjYCOwFnlXVEhFZISIrvIetA8qAUuBXwOe824cBb4nIbmAbsFZVNwQrq+nc5tJavrRqF7NHDOGRZbOIivLXK2hM8KUkxPLkA3NIjovhk7/ZxvG6RqcjhT0Jp4s+eXl5WlBgt0z0lqLysyxbuYXcIUk8+5l5DEqKdTqSMRw8cY47H9vCkKRYnvvsVaQNiHc6Ur8mIjs6u5XA7qQ2fh2ubeC+325jcFIcTz4wx4qDCRnjh6Xwm/uuoLq+mft+u82GvwaRFQjzD/ZW1fPRx7egwNOfmkPmIFtb2oSWy0cO4Zf3XM6+qnN8/FfvcOp8i9ORwpIVCPMB24/U8dHHtxAtwp+Wz2WMre9gQtT1EzJY+cnLOXjyHHc9toXy03ZNordZgTDv+du+E9z763dIHxDPc5+dx/hhKU5HMuaibpg4jGc+dSW151u485dbOGhTcvQqKxAGVeX37xzln5/awfiMFP68Yh65Q5KcjmVMQPJGDeVPn5mHS5W7Ht/C26V2n0RvsQIR4Rpa2vnyn3bxzReLuXpcGn9cPpdUGxVi+plJWQN5foVnRNM9v36Hn796ELc7fEZoOsUKRAQrPXmOpY++zcu7K/nKTZfx2/uuYEC8Td1t+qcRqUm8/OB8ls7I5v9tOsD9v9tOXUOr07H6NSsQEcjlVp7acoRb//dtTje08vQDV/KFD4+3m+BMv5ccH8NPPjaT7902lS2HTrHokTf5274TTsfqt6xARJiSyrN85Jeb+fbLJVw+cghrv/Ahrh5vcyuZ8CEi3DN3JM9/9ioGxMfwwO8K+Nzvd3Ci3pYv7S67kzpCnG1s49G/l/Lrtw4zJCmWby2ezK0zshGxswYTvlrb3fzqzTJ+9upB4qOj+MrNl/GJK0cSF2OfjS+42J3UViDCXH1zG7996whPvFXGueZ27p4znG/kT2RwUpzT0YzpM0dqG/j3l4p5q7SWnMGJfP6GcdxxeS6x0VYorEBEoJpzLfxx2zF+/dZhzja1cfPkYXzpxsuYnD3Q6WjGOEJVef1ADT/560F2Hz/D8KGJfO66cSydmR3R66pbgYgQqso7h+t4ZutRNpZU0+ZSbpiYwZdvvIxpuYOcjmdMSFBVXtt/kp9sOkhRxVlS4mP4yOwcPjF3JJdF4M2hViDCmKpSWH6WDSXVrC+q4sipRgYmxHDn5cP5+JUjGJdhU2UY44+qUnD0NL/fepR1RdW0utzMyB1E/tQsFkzNZFRastMR+4QViDBz6nwLW8vq2FJWy2v7aqg400R0lHDV2FRunZHN4unZJMbZetHGBKquoZXndhxnTWEVheVnAZiYmcJ1EzKYO2YoV4waSnKY3iNkBaIfa25zsa/6HMUVZympPMuOo6c5cOI8AElx0Vw1NpX8qVncOCnDLjwb0wsqzjSxsbiaDSXVvHvsNG0uJSZKmJY7iJnDBzM1exBTcwYxNj2ZmDC4yO1YgRCRfOBnQDTwhKp+v8N+8e5fCDQC96nqzkDa+tNfC0R9cxtVZ5qpPNtExekmjtQ2UFbbwOHaBo7VNeLyThkwOCmWaTmDmDsmlXljU5mWM8hGYRgTRI2t7ew4epqtZad4p6yOksp6mtpcAMTHRDE6Lfm9x6i0ZHIGJ5I1KIHswYkkxPaPs3hHCoSIRAMHgJuAcjxrVN+tqnt8jlkIfB5PgbgS+JmqXhlIW3/6skC43Uqry02by01Lu+fR3Oaipc1NU5uLxtZ2Glo8z+ea2znX3EZ9cztnG9uoa2zl1PkWTjW0UnuuhYZW1wfeOyE2itFpAxiTlsyY9GSmZA9ias5AcgYn2n0LxjjI5VYO156nuKKePVX1lNWcp6y2gWOnGmnvMPfToMRY0gbEkZocT+qAOIYkxzEwIZaBiTGkJMSSEh9DUlw0yd7nhNgLjyjiY6KJi4kiNlqIjYoK6iwHFysQwexUmwOUqmqZN8QqYCng+5/8UuAp9VSprSIyWESygFEBtO01ix55k6Y2F6rgVsXlVlQ9PwwuVdxupd2ttLvcnme3vvepvjsSY6MZmBjDkKQ40gbEM3xoEkOT48galEDWoESyB3s+eQxLSbBpL4wJQdFRwriMFMZlpHDbrJz3tre73FScaaLyTDNVZ5uoOttM9dlm6hpaOdXQwsGT5znT2EZ9UxutLne3v29MlBATLcRERXmfhSgRor3PaSnxvPzg/N78o3q+b6+/4/tygOM+r8vxnCV0dUxOgG0BEJHlwHKAESNG9CjoZcNSaHO5iRIhSjy36nv+8nnvHyA2OoroKHnvHyo2Ooq4mCjioqOIjY4iITaKhNho4mM8zxc+FSTFxZCSEMPAhFi7e9OYMBUTHcXI1GRGpnY98qm5zcXZpjYaWtppbHXR0NJOQ2s7LW1umttdNLe5aWlz0eby9FK0tnt6Klxupc2luNxu2tyeD64XPsQGa5LNYBYIfx+BO37s7uyYQNp6NqquBFaCp4upOwEv+MnHZvakmTHGdNuFrqT+IJgFohwY7vM6F6gM8Ji4ANoaY4wJomD2eWwHxovIaBGJA5YBqzscsxr4pHjMBc6qalWAbY0xxgRR0M4gVLVdRB4CNuIZqvobVS0RkRXe/Y8B6/CMYCrFM8z1/ou1DVZWY4wx/8hulDPGmAh2sWGuNqzGGGOMX1YgjDHG+GUFwhhjjF9WIIwxxvgVVhepRaQGONrD5mlAbS/G6S2hmgtCN1uo5oLQzRaquSB0s4VqLuhetpGqmu5vR1gViEshIgWdXcl3UqjmgtDNFqq5IHSzhWouCN1soZoLei+bdTEZY4zxywqEMcYYv6xAvG+l0wE6Eaq5IHSzhWouCN1soZoLQjdbqOaCXspm1yCMMcb4ZWcQxhhj/LICYYwxxi8rEH6IyFdFREUkzeksACLyXREpFJFdIvIXEcl2OtMFIvJDEdnnzfeiiAx2OhOAiNwlIiUi4hYRx4ciiki+iOwXkVIRedjpPBeIyG9E5KSIFDudpSMRGS4ir4nIXu+/5RedzgQgIgkisk1Edntz/YfTmXyJSLSIvCsiay71vaxAdCAiw4GbgGNOZ/HxQ1WdrqozgTXAt50O5GMTMFVVpwMHgH91OM8FxcBHgDecDiIi0cCjwAJgMnC3iEx2NtV7fgfkOx2iE+3Av6jqJGAu8GCI/L21ADeo6gxgJpDvXc8mVHwR2Nsbb2QF4h/9BPg6nSxx6gRVrfd5mUxoZfuLqrZ7X27Fs/qf41R1r6rudzqH1xygVFXLVLUVWAUsdTgTAKr6BlDndA5/VLVKVXd6vz6H5z+9HGdTgXqc976M9T5C4ndSRHKBRcATvfF+ViB8iMitQIWq7nY6S0ci8l8ichz4BKF1BuHrAWC90yFCUA5w3Od1OSHwH11/IiKjgFnAO84m8fB24+wCTgKbVDUkcgE/xfMB190bbxbMNalDkoj8Fcj0s+ubwL8BN/dtIo+L5VLVl1X1m8A3ReRfgYeA74RKNu8x38TTJfD7UMoVIsTPtpD4xNkfiMgA4HngSx3Oph2jqi5gpvea24siMlVVHb2OIyKLgZOqukNEruuN94y4AqGqN/rbLiLTgNHAbhEBT1fJThGZo6rVTuXy4w/AWvqwQHSVTUT+CVgMfFj78MaabvydOa0cGO7zOheodChLvyIisXiKw+9V9QWn83SkqmdE5O94ruM4faF/PnCriCwEEoCBIvKMqt7T0ze0LiYvVS1S1QxVHaWqo/D8Us/ui+LQFREZ7/PyVmCfU1k6EpF84BvArara6HSeELUdGC8io0UkDlgGrHY4U8gTzye1XwN7VfXHTue5QETSL4zWE5FE4EZC4HdSVf9VVXO9/38tA/52KcUBrED0F98XkWIRKcTTBRYSw/28/hdIATZ5h+E+5nQgABG5XUTKgXnAWhHZ6FQW70X8h4CNeC60PquqJU7l8SUifwS2ABNEpFxEPuV0Jh/zgXuBG7w/W7u8n46dlgW85v193I7nGsQlDykNRTbVhjHGGL/sDMIYY4xfViCMMcb4ZQXCGGOMX1YgjDHG+GUFwhhjjF9WIIwxxvhlBcIYY4xfViCMCRIRucK7TkaCiCR71w6Y6nQuYwJlN8oZE0Qi8j088+IkAuWq+t8ORzImYFYgjAki79xL24Fm4CrvLKDG9AvWxWRMcA0FBuCZryrB4SzGdIudQRgTRCKyGs8KcqOBLFV9yOFIxgQs4taDMKaviMgngXZV/YN3XerNInKDqv7N6WzGBMLOIIwxxvhl1yCMMcb4ZQXCGGOMX1YgjDHG+GUFwhhjjF9WIIwxxvhlBcIYY4xfViCMMcb49f8BhRFrhQsMwW8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "def normal_distribution(x, mean, standard_deviation):\n",
+    "    return math.exp(-0.5 * pow( (x - mean) / standard_deviation, 2)) / standard_deviation / math.sqrt(2 * math.pi)\n",
+    "\n",
+    "\n",
+    "\n",
+    "mean = 0\n",
+    "standard_deviation = 1\n",
+    "\n",
+    "x_array = np.arange(-4, 4, 0.1)\n",
+    "y_array = []\n",
+    "for x in x_array:\n",
+    "    y = normal_distribution(x, mean, standard_deviation)\n",
+    "    y_array = y_array + [y]\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x_array, y_array, \"-\")\n",
+    "ax.set_xlabel(\"x\")\n",
+    "ax.set_ylabel(\"probability\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.11820545 -0.34614928 -0.42576096 ...  1.57779068 -1.24144681\n",
+      "  0.76370401]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# generate random numbers following a normal distribution\n",
+    "x = random.normal(loc=0, scale=2, size=10000)\n",
+    "\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make a 2 dimensional distribution of random numbers and plot it\n",
+    "\n",
+    "x = random.normal(loc=0, scale=2, size=1000)\n",
+    "y = random.normal(loc=0, scale=2, size=1000)\n",
+    "\n",
+    "plt.plot(x, y, \".\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = random.normal(loc=0, scale=2, size=1000)\n",
+    "y = random.normal(loc=0, scale=2, size=1000)\n",
+    "\n",
+    "data = [x, y]\n",
+    "\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "ax1.set_title('Box Plot')\n",
+    "ax1.boxplot(data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Biomodal distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.83396869  2.46855613  0.18863638 ...  8.72127243 10.24225462\n",
+      "  7.26607252]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# generate random numbers following a bi-modal distribution\n",
+    "a = random.normal(loc=0, scale=2, size=10000) \n",
+    "b = random.normal(loc=8, scale=2, size=10000)\n",
+    "\n",
+    "x = np.concatenate([a, b])\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Paired/related samples "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "number_of_samples = 100\n",
+    "x = random.uniform(low=0, high=100, size=number_of_samples)\n",
+    "\n",
+    "x1 = x + random.normal(loc=0, scale=2, size=number_of_samples)\n",
+    "x2 = x + random.normal(loc=0, scale=2, size=number_of_samples)\n",
+    "\n",
+    "\n",
+    "plt.plot(x1, x2, \".\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recap: Descriptive statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 0.0006667702810900537\n",
+      "standard_deviation: 1.9981803784765388\n"
+     ]
+    }
+   ],
+   "source": [
+    "# we setup an array of normal distributed values and \n",
+    "# measure their mean and standard deviation.\n",
+    "x = random.normal(loc=0, scale=2, size=1000000) # <-- increase and decrease \n",
+    "                                                #     the size here!\n",
+    "\n",
+    "mean = np.mean(x)\n",
+    "standard_deviation = np.std(x)\n",
+    "\n",
+    "print(\"Mean: \" + str(mean))\n",
+    "print(\"standard_deviation: \" + str(standard_deviation))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Central limit theorem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def normal_random_plots(num_random_numbers):\n",
+    "    x = random.normal(loc=0, scale=1, size=num_random_numbers)\n",
+    "\n",
+    "    data = [x]\n",
+    "    \n",
+    "    fig1, ax1 = plt.subplots()\n",
+    "    ax1.set_title('Probability distribution of ' + str(num_random_numbers) + ' normal distributed random numbers')\n",
+    "    ax1.set_xlabel(\"x\");\n",
+    "    ax1.set_ylabel(\"probability\");\n",
+    "    ax1.hist(data)\n",
+    "    plt.show()\n",
+    "\n",
+    "for i in [1, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000]:\n",
+    "    normal_random_plots(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def normal_random_box_plots(num_random_numbers):\n",
+    "    x = random.normal(loc=0, scale=1, size=num_random_numbers)\n",
+    "    y = random.normal(loc=0, scale=1, size=num_random_numbers)\n",
+    "\n",
+    "    data = [x, y]\n",
+    "\n",
+    "    fig1, ax1 = plt.subplots()\n",
+    "    ax1.set_title('Box Plot of ' + str(num_random_numbers) + ' normal distributed random numbers')\n",
+    "    ax1.boxplot(data)\n",
+    "    plt.show()\n",
+    "\n",
+    "for i in [1, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000]:\n",
+    "    normal_random_box_plots(i)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Vdd6SVyxJGmlsoFfVScBJAEn2B/7jUJhTVY+bu53kdOB8w1yStq5Nvg49ybFJjl3KYiRJm26SKZcHVNU6YF1/e+QHoFV11OYWJUlaPL8pKkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRkwc6ElWJLk8yfkj2l6R5Kr+50tJnr60ZUqSxlm5iL7HAxuAnUe0XQ88t6ruSHIwsBZ49hLUJ0ma0ERn6ElWA4cCp41qr6ovVdUd/d1LgNVLU54kaVKTTrmcApwA3D9B31cBF4xqSHJMkpkkM7OzsxMOLUmaxNhAT/JCYGNVrZ+g7+/TBfqJo9qram1VTVfV9NTU1KKLlSTNb5I59P2Aw5McAqwCdk5yVlUdMdgpyb+nm5I5uKp+sPSlSpIWMvYMvapOqqrVVbUGeBlw4Ygw/w3gXOCVVfXNLVKpJGlBi7nK5UGSHAtQVacCbwAeBbwzCcC9VTW9JBVKkiaSqlqWgaenp2tmZmZZxpakbVWS9fOdMPtNUUlqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNWLiQE+yIsnlSc4f0ZYk/yfJtUmuSvI7S1umJGmcxZyhHw9smKftYOC3+p9jgHdtZl2SpEWaKNCTrAYOBU6bp8uLgDOrcwmwa5I9lqhGSdIEJj1DPwU4Abh/nva9gO8M3L+5f+xBkhyTZCbJzOzs7KIKlSQtbGygJ3khsLGq1i/UbcRj9ZAHqtZW1XRVTU9NTS2iTEnSOJOcoe8HHJ7kBuAjwAFJzhrqczOw98D91cAtS1KhJGkiYwO9qk6qqtVVtQZ4GXBhVR0x1O0TwJH91S7/Abirqm5d+nIlSfNZualPTHIsQFWdCnwKOAS4FvgJcPSSVCdJmtiiAr2q1gHr+tunDjxewGuWsjBJ0uL4TVFJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDVibKAnWZXksiRXJrk6yRtH9NklyScH+hy9ZcqVJM1n5QR97gEOqKq7k2wPXJzkgqq6ZKDPa4BrquqwJFPAN5J8sKp+viWKliQ91NhAr6oC7u7vbt//1HA3YKckAXYEbgfuXcI6JUljTDSHnmRFkiuAjcBnq+rSoS7vAJ4M3AJ8FTi+qu4fsZxjkswkmZmdnd3M0iVJgyYK9Kq6r6r2AVYDz0rytKEuLwCuAPYE9gHekWTnEctZW1XTVTU9NTW1maVLkgYt6iqXqroTWAccNNR0NHBuda4Frgd+e0kqlCRNZJKrXKaS7Nrf3gE4EPj6ULebgOf1fR4DPAm4bmlLlSQtZJKrXPYAzkiygu4XwNlVdX6SYwGq6lTgTcDpSb4KBDixqm7bUkVLkh5qkqtcrgKeMeLxUwdu3wI8f2lLkyQtht8UlaRGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI8YGepJVSS5LcmWSq5O8cZ5++ye5ou/zxaUvVZK0kJUT9LkHOKCq7k6yPXBxkguq6pK5Dkl2Bd4JHFRVNyV59BaqV5I0j7GBXlUF3N3f3b7/qaFuLwfOraqb+udsXMoiJUnjTTSHnmRFkiuAjcBnq+rSoS5PBHZLsi7J+iRHzrOcY5LMJJmZnZ3dvMolSQ8yUaBX1X1VtQ+wGnhWkqcNdVkJPBM4FHgB8PokTxyxnLVVNV1V01NTU5tZuiRp0KKucqmqO4F1wEFDTTcDn66qH1fVbcBFwNOXpEJJ0kQmucplqv/QkyQ7AAcCXx/q9nHgOUlWJnkE8Gxgw1IXK0ma3yRXuewBnJFkBd0vgLOr6vwkxwJU1alVtSHJp4GrgPuB06rqa1usaknSQ6S7iGXrm56erpmZmWUZW5K2VUnWV9X0qDa/KSpJjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEaMDfQkq5JcluTKJFcneeMCffdNcl+SlyxtmZKkcVZO0Oce4ICqujvJ9sDFSS6oqksGOyVZAbwV+MwWqFOSNMbYM/Tq3N3f3b7/qRFdjwPOATYuXXmSpElNNIeeZEWSK+jC+rNVdelQ+17Ai4FTxyznmCQzSWZmZ2c3tWZJ0ggTBXpV3VdV+wCrgWcledpQl1OAE6vqvjHLWVtV01U1PTU1tWkVS5JGmmQO/QFVdWeSdcBBwNcGmqaBjyQB2B04JMm9VXXeUhUq6ZfEybtspXHu2jrjNGRsoCeZAn7Rh/kOwIF0H34+oKoeN9D/dOB8w1xqlEH7S2uSM/Q9gDP6q1i2A86uqvOTHAtQVQvOm0uSto6xgV5VVwHPGPH4yCCvqqM2vyxJ0mL5TVFJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUiFSN+jtbW2HgZBa4cVkGb9PuwG3LXYQ0gsfm0npsVY382ynLFuhaWklmqmp6ueuQhnlsbj1OuUhSIwx0SWqEgd6OtctdgDQPj82txDl0SWqEZ+iS1AgDXZIaYaBv45K8L8nGJF8b31vaepLsneQLSTYkuTrJ8ctdU+ucQ9/GJfk94G7gzKoa/l+v0rJJsgewR1V9JclOwHrgD6rqmmUurVmeoW/jquoi4PblrkMaVlW3VtVX+ts/AjYAey1vVW0z0CVtcUnW0P3ns0uXt5K2GeiStqgkOwLnAH9dVT9c7npaZqBL2mKSbE8X5h+sqnOXu57WGeiStogkAd4LbKiq/7Xc9fwqMNC3cUk+DHwZeFKSm5O8arlrknr7Aa8EDkhyRf9zyHIX1TIvW5SkRniGLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI/4/M5YbmogAt/IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HgEIR4ABQKAIcAApFgANAoQjwcWLatGmyPawfScOaf9q0aWO8lQC6qaOPEaJ71q5dq4gY0TYGQx/AxMAIHEBTo3F2yBliexiBA2hqNM4OJc4Q28EIHAAKRYADQKEIcAAoFAEOAIUiwAGgUAQ4ABSKAAeAQhHgAFAoAhwACkWAA0ChCHAAKBQBDgCFIsABoFAEOAAUigAHgEIR4ABQKAIcAApFgANAoQhwACgUAQ4AhSLAAaBQBDgAFKqjALe9g+3Lbd9qe4XtV3arMABAcz0dLv85Sd+LiDfY3krSNl2oCQDQgrYD3PZ2kg6UdIIkRcQTkp7oTlkAgKF0cgllT0kDki6y/Qvb59vetnYm27NtL7G9ZGBgoIPmAABVnQR4j6SXSPqviNhP0u8knVk7U0TMjYj+iOjv7e3toDkAQFUnAb5S0sqIuCHfv1wp0AEAo6DtAI+IByTda3vv/NBrJd3SlaoAAEPq9FMo75a0IH8C5Q5JJ3ZeEgCgFR0FeEQsk9TfpVoAAMPAX2ICQKEIcAAoFAEOAIUiwAGgUAQ4ABSKAAeAQhHgAFAoAhwACkWAA0ChCHAAKBQBDgCFIsABoFAEOAAUigAHgEJ1+v/AAUxwcfZ20pztR6cdDAsBPk6MxkHCAYJ2+MPrFREj346tmDPizUwoBPg4MRoHCQcIMLFwDRwACkWAA0ChCHAAKBQBDgCFIsABoFAEOAAUigAHgEIR4ABQKAIcAApFgANAoQhwACgUAQ4AhSLAAaBQBDgAFKrjALc9yfYvbF/djYIAAK3pxgj8NEkrurAeAMAwdBTgtneT9DeSzu9OOQCAVnU6Aj9X0hmS/tBoBtuzbS+xvWRgYKDD5gAAg9oOcNtHSFodEUubzRcRcyOiPyL6e3t7220OAFCjkxH4AZL+1vZdki6VdLDti7tSFQBgSG0HeEScFRG7RUSfpGMk/TAi3tK1ygAATfE5cAAoVE83VhIRiyQt6sa6AACtYQQOAIUiwAGgUAQ4ABSKAAeAQhHgAFAoAhwACkWAA0ChCHAAKBQBDgCFIsABoFAEOAAUigAHgEIR4ABQKAIcAApFgANAoQhwACgUAQ4AhSLAAaBQBDgAFIoAB4BCEeAAUCgCHAAKRYADQKEIcAAoVM9YF4Cn2R7R9e+4444jun5MXCPdNyX6ZzsI8HEiIoa9jO22lgOGg745fnEJBQAKRYADQKEIcAAoFAEOAIVqO8Bt7277R7ZX2F5u+7RuFgYAaK6TT6FslPS+iLjR9lRJS23/ICJu6VJtAIAm2h6BR8SqiLgx335E0gpJu3arMABAc125Bm67T9J+km6oM2227SW2lwwMDHSjOQCAuhDgtqdIukLS6RGxvnZ6RMyNiP6I6O/t7e20OQBA1lGA295SKbwXRMTC7pQEAGhFJ59CsaQLJK2IiM90ryQAQCs6GYEfIGmWpINtL8s/h3epLgDAENr+GGFEXCtp5P9FGQCgLv4SEwAKRYADQKEIcAAoFAEOAIUiwAGgUAQ4ABSKAAeAQhHgAFAoAhwACkWAA0ChCHAAKBQBDgCFIsABoFAEOAAUigAHgEIR4ABQKAIcAApFgANAoQhwACgUAQ4AhSLAAaBQBDgAFIoAB4BCEeAAUCgCHAAKRYADQKEIcAAoFAEOAIUiwAGgUAQ4ABSKAAeAQnUU4LYPs/0r27fbPrNbRQEAhtZ2gNueJOlLkv5a0kxJx9qe2a3CAADNdTICf7mk2yPijoh4QtKlko7qTlkAgKH0dLDsrpLurdxfKWn/2plsz5Y0W5JmzJjRQXN/mmy3NT0iRqIcYBPN+id9c+R1MgKvt3c22zMRMTci+iOiv7e3t4Pm/jRFRFs/wGigb46tTgJ8paTdK/d3k3R/Z+UAAFrVSYD/XNLzbD/H9laSjpF0VXfKAgAMpe1r4BGx0fYpkv5H0iRJF0bE8q5VBgBoqpM3MRUR35H0nS7VAgAYBv4SEwAKRYADQKEIcAAoFAEOAIXyaH6w3vaApLtHrcGJb7qkh8a6CKAO+mZ37RERm/0l5KgGOLrL9pKI6B/rOoBa9M3RwSUUACgUAQ4AhSLAyzZ3rAsAGqBvjgKugQNAoRiBA0ChCHAAKBQBXiDbF9pebfvmsa4FqLK9u+0f2V5he7nt08a6pomMa+AFsn2gpA2S5kfEvmNdDzDI9s6Sdo6IG21PlbRU0tERccsYlzYhMQIvUEQslvTwWNcB1IqIVRFxY779iKQVSt+fixFAgAMYEbb7JO0n6YaxrWTiIsABdJ3tKZKukHR6RKwf63omKgIcQFfZ3lIpvBdExMKxrmciI8ABdI1tS7pA0oqI+MxY1zPREeAFsn2JpOsl7W17pe2TxromIDtA0ixJB9teln8OH+uiJio+RggAhWIEDgCFIsABoFAEOAAUigAHgEIR4ABQKAIcAApFgANAof4fr9ECh63zcMkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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458AvAc4BLgO+2q/rtAnVJUmax6pxFq6qU4FTJ1SLJGkR/EtMSWqUAS5JjTLAJalRBvg2Yq+99iLJon6ARc2/1157rfCzVIuWo2/aP5dmrIuYmpzNmzdTVVu1jekdS1qM5eibYP9cCkfgktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktSosQI8yR5Jzkny9SQbk/zMpAqTJM1t1ZjLvwf4dFW9NMnOwG4TqEmStABLDvAkjwCeC5wAUFX3AfdNpixJ0nzGOYVyILAJ+GCSryR5f5KHj86U5MQkG5Js2LRp0xjNSZKGxgnwVcDTgL+sqqcC3wfeOjpTVZ1WVeuqat3U1NQYzUmShsYJ8BuAG6rqkv7+OXSBLklaBksO8Kr6DnB9kif2Dz0fuHIiVUmS5jXup1DeAJzdfwLlKuA145ckSVqIsQK8qi4H1k2oFknSIviXmJLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUeP+P3BNSJ36CFi/+9ZvQ1qk5eibP25Hi5KqWrbG1q1bVxs2bFi29lqShK29LZajDW1/lqvf2D9nl+TSqnrIdy94CkWSGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGjR3gSXZM8pUkn5xEQZKkhZnECPwUYOME1iNJWoSxAjzJfsC/B94/mXIkSQs17gj83cBbgB/NNkOSE5NsSLJh06ZNYzYnSZq25ABP8mLg1qq6dK75quq0qlpXVeumpqaW2pwkacQ4I/DDgP+Q5BrgI8DhSc6aSFWSpHktOcCr6m1VtV9VrQWOBT5fVa+aWGWSpDn5OXBJatSqSaykqi4ALpjEuiRJC+MIXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1KhVK12AHpRkq65/zz333Krrl7S8DPBtRFUtepkkS1pOWqytPbgABxhLYYBLmpODi22X58AlqVEGuCQ1ygCXpEYtOcCT7J/kC0k2JrkiySmTLEySNLdxLmI+ALy5qi5Lsga4NMlnq+rKCdUmSZrDkkfgVXVzVV3W3/4esBHYd1KFSZLmNpFz4EnWAk8FLplh2olJNiTZsGnTpkk0J0liAgGeZDVwLvDGqrprdHpVnVZV66pq3dTU1LjNSZJ6YwV4kp3owvvsqjpvMiVJkhZinE+hBPgAsLGq3jW5kiRJCzHOCPww4Djg8CSX9z9HTqguSdI8lvwxwqr6ErD1/8ONJGlG/iWmJDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNGivAkxyR5J+TfCvJWydVlCRpfksO8CQ7An8O/AJwEPDyJAdNqjBJ0tzGGYE/E/hWVV1VVfcBHwGOnkxZkqT5rBpj2X2B6wf3bwAOHZ0pyYnAiQAHHHDAGM3965RkSdOramuUI21hrv5p39z6xhmBz7R1HrJlquq0qlpXVeumpqbGaO5fp6pa0o+0HOybK2ucAL8B2H9wfz/gpvHKkSQt1DgB/o/AE5I8LsnOwLHAJyZTliRpPks+B15VDyQ5Cfg/wI7A6VV1xcQqkyTNaZyLmFTVp4BPTagWSdIi+JeYktQoA1ySGmWAS1KjDHBJalSW84P1STYB1y5bg9u/vYHbVroIaQb2zcl6bFU95C8hlzXANVlJNlTVupWuQxpl31wenkKRpEYZ4JLUKAO8baetdAHSLOyby8Bz4JLUKEfgktQoA1ySGmWANyjJ6UluTfK1la5FGkqyf5IvJNmY5Iokp6x0Tdszz4E3KMlzgbuBM6vqkJWuR5qW5DHAY6rqsiRrgEuBY6rqyhUubbvkCLxBVXUhcMdK1yGNqqqbq+qy/vb3gI1035+rrcAAl7RVJFkLPBW4ZGUr2X4Z4JImLslq4FzgjVV110rXs70ywCVNVJKd6ML77Ko6b6Xr2Z4Z4JImJkmADwAbq+pdK13P9s4Ab1CSDwMXA09MckOS1650TVLvMOA44PAkl/c/R650UdsrP0YoSY1yBC5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqP+P+/2Eu9dbCVDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def uniform_random_box_plots(num_random_numbers):\n",
+    "    x = random.uniform(low=0, high=10, size=num_random_numbers)\n",
+    "    y = random.uniform(low=0, high=10, size=num_random_numbers)\n",
+    "\n",
+    "    data = [x, y]\n",
+    "\n",
+    "    fig1, ax1 = plt.subplots()\n",
+    "    ax1.set_title('Box Plot of ' + str(num_random_numbers) + ' normal distributed random numbers')\n",
+    "    ax1.boxplot(data)\n",
+    "    plt.show()\n",
+    "\n",
+    "for i in [1, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000]:\n",
+    "    uniform_random_box_plots(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Students grades"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from numpy import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "student_count = 60\n",
+    "grades = random.normal(loc=3, scale=1, size=student_count)\n",
+    "\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "ax1.set_title('Probability distribution grades')\n",
+    "ax1.set_xlabel(\"grade\");\n",
+    "ax1.set_ylabel(\"count\");\n",
+    "ax1.hist(grades, range=(1,6), bins=6)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "student_count = 60\n",
+    "grades = random.normal(loc=2.5, scale=0.8, size=student_count)\n",
+    "\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "ax1.set_title('Probability distribution grades')\n",
+    "ax1.set_xlabel(\"grade\");\n",
+    "ax1.set_ylabel(\"likelihood\");\n",
+    "ax1.hist(grades, range=(1,6), bins=6, density=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3AHsM020FMEfS/pKmAicByzq2+1jgcuBU2z+tlU+TtOfQMnAM8KPR7VpERGyLbq9iutD28nqBpIfa/pPteU0dbG+SdBZwFTAFuMj2KklnlPrFwHuAfYGPSgLYVLb3KOCKUrYrcIntr45+9yIiYqy6TRDnAss7yv6LaohpWCWpLO8oW1xbfjXVkFVnvzXAwZ3lERExcbaaICTtR3Xl0cMkPZ2/nHjei2q4KSIiJqmRjiCeR3VieibwwVr5vcA7W4opIiK2A1tNELYvBi6W9GLbX5igmCIiYjsw0hDTy21/Bpgt6S2d9bY/2NAtIiImgZGGmKaV9+EuZY2IiElqpCGmfyvv75uYcCIiYnsx0hDTR7ZWb/uN4xtORERsL0YaYlo5IVFERMR2p5urmCIiYic00hDTh2y/WdKXaJiJ1fYLW4ssIiJ6aqQhpk+X9w+0HUhERGxfRhpiWlnery0zsh5IdSRxa3lKXERETFJdTdYn6XhgMfBzqvmY9pf0Wttf2XrPiIjYUXU7m+u/AEfbXg0g6QnAlWz+iNCIiJhEun2i3Iah5FCsATa0EE9ERGwnRrqK6W/L4ipJy4HLqM5B/B3VE+MiImKSGmmI6QW15d8CR5XlQWCfViKKiIjtwkhXMZ22LRuXdCzwYapHjl5o+7yO+lOAt5fVjcDrbP+wm74REdGubq9i2g14FfBkYLehctunb6XPFOAC4LnAALBC0jLbt9Sa/QI4yvZdkuYDS4DDuuwbEREt6vYk9aeB/aieMHct1RPm7h2hz6HAattryj0TS4EF9Qa2v2P7rrL63bLdrvpGRES7uk0QB9h+N/D7Mj/T8cBfj9BnBrCutj5QyobzKv5y2WzXfSUtlNQvqX9wcHCEkCIiolvdJoj7y/vdkp4CPByYPUIfNZRtMZ8TgKSjqRLE0PmIrvvaXmJ7nu15fX19I4QUERHd6vZGuSWS9gHeDSyjesLcu0foMwDMqq3PBNZ3NpL0VOBCYL7tO0bTNyIi2tNVgrB9YVm8Fnh8l9teAcyRtD/wa+Ak4GX1BpIeC1wOnGr7p6PpGxER7er2KqZ9gbOBI6iGeq4Hzqn9xb8F25sknQVcRXWp6kW2V0k6o9QvBt4D7At8VBLApjJc1Nh3jPsYERFj0O0Q01LgOuDFZf0U4FLgOVvrZHs5sLyjbHFt+dXAq7vtGxERE6fbBPEI2+fU1s+VdEIbAUVExPah26uYvinpJEm7lNeJVLO5RkTEJDXSZH33Up1zEPAW4DOlaheqqTHe22p0ERHRMyPNxbTnRAUSERHbl27PQSDphcCRZfVbtr/cTkgREbE96OochKTzgDcBt5TXm0pZRERMUt0eQRwHPM32gwCSLgZuABa1FVhERPRWt1cxAexdW374eAcSERHbl26PIN4P3CDpm1RXNB0JvKO1qCIioudGTBCSdgEeBA4HDqFKEG+3fVvLsUVERA+NmCBsPyjpLNuXUc3kGhERO4Fuz0FcI+mtkmZJesTQq9XIIiKip7o9B3E61R3Vr+8o73bq74iI2MF0myAOokoOz+Qv030v3mqPiIjYoXWbIC4G7gE+UtZPLmUnthFURET0XrcJ4om2D66tf1PSD9sIKCIitg/dnqS+QdLhQyuSDgP+s52QIiJie9DtEcRhwCsk/aqsPxb4saSbAdt+aivRRUREz3SbII4dy8YlHQt8mOq50hfaPq+j/kDgE8Bc4B9tf6BWtxa4F3iA8qzqscQQERFj01WCsP3L0W5Y0hTgAuC5wACwQtIy27fUmt0JvBEY7vGlR9u+fbSfHRER2240k/WN1qHAattrbN8HLAUW1BvY3mB7BXB/i3FERMQYtJkgZgDrausDpaxbBq6WtFLSwuEaSVooqV9S/+Dg4BhDjYiITm0mCDWUeRT9j7A9F5gPnCnpyKZGtpfYnmd7Xl9f31jijIiIBm0miAFgVm19JrC+286215f3DcAVVENWERExQdpMECuAOZL2lzQVOIkuZ4OVNE3SnkPLwDHAj1qLNCIittDtZa6jZnuTpLOAq6guc73I9ipJZ5T6xZL2A/qBvYAHJb2Zat6n6cAVkoZivMT2V9uKNSIittRaggCwvRxY3lG2uLZ8G9XQU6d7gIMbyiMiYoK0OcQUERE7sCSIiIholAQRERGNkiAiIqJREkRERDRKgoiIiEatXua6I5m96MpehxARsV3JEURERDRKgoiIiEZJEBER0SgJIiIiGiVBREREoySIiIholAQRERGNkiAiIqJREkRERDRKgoiIiEatJghJx0q6VdJqSYsa6g+U9F+S/iTpraPpGxER7WotQUiaAlwAzKd6zvTJkg7qaHYn8EbgA2PoGxERLWrzCOJQYLXtNbbvA5YCC+oNbG+wvQK4f7R9IyKiXW0miBnAutr6QCkb176SFkrql9Q/ODg4pkAjImJLbSYINZR5vPvaXmJ7nu15fX19XQcXERFb12aCGABm1dZnAusnoG9ERIyDNhPECmCOpP0lTQVOApZNQN+IiBgHrT1RzvYmSWcBVwFTgItsr5J0RqlfLGk/oB/YC3hQ0puBg2zf09S3rVgjImJLrT5y1PZyYHlH2eLa8m1Uw0dd9Y2IiImTO6kjIqJREkRERDRqdYgpIiaf2Yuu7HUI42Ltecf3OoTtXo4gIiKiURJEREQ0SoKIiIhGSRAREdEoCSIiIholQURERKMkiIiIaJQEERERjZIgIiKiURJEREQ0SoKIiIhGSRAREdEoCSIiIholQURERKNWE4SkYyXdKmm1pEUN9ZL0kVJ/k6S5tbq1km6WdKOk/jbjjIiILbX2PAhJU4ALgOcCA8AKScts31JrNh+YU16HAR8r70OOtn17WzFGRMTw2jyCOBRYbXuN7fuApcCCjjYLgE+58l1gb0mPbjGmiIjoUpsJYgawrrY+UMq6bWPgakkrJS0c7kMkLZTUL6l/cHBwHMKOiAhoN0GoocyjaHOE7blUw1BnSjqy6UNsL7E9z/a8vr6+sUcbERGbaTNBDACzauszgfXdtrE99L4BuIJqyCoiIiZImwliBTBH0v6SpgInAcs62iwDXlGuZjoc+J3t30iaJmlPAEnTgGOAH7UYa0REdGjtKibbmySdBVwFTAEusr1K0hmlfjGwHDgOWA38ATitdH8UcIWkoRgvsf3VtmKNiIgttZYgAGwvp0oC9bLFtWUDZzb0WwMc3GZsERGxdbmTOiIiGiVBREREoySIiIholAQRERGNkiAiIqJREkRERDRKgoiIiEZJEBER0SgJIiIiGiVBREREoySIiIholAQRERGNWp2sLyJiezV70ZW9DmHcrD3v+Fa2myOIiIholAQRERGNkiAiIqJREkRERDRKgoiIiEatJghJx0q6VdJqSYsa6iXpI6X+Jklzu+0bERHtai1BSJoCXADMBw4CTpZ0UEez+cCc8loIfGwUfSMiokVtHkEcCqy2vcb2fcBSYEFHmwXAp1z5LrC3pEd32TciIlrU5o1yM4B1tfUB4LAu2szosi8AkhZSHX0AbJR06xjjnQ7cPsa+O6rs8+S3s+0v7IT7rPO3aZ8fN1xFmwlCDWXusk03fatCewmwZHShbUlSv+1527qdHUn2efLb2fYXss/jqc0EMQDMqq3PBNZ32WZqF30jIqJFbZ6DWAHMkbS/pKnAScCyjjbLgFeUq5kOB35n+zdd9o2IiBa1dgRhe5Oks4CrgCnARbZXSTqj1C8GlgPHAauBPwCnba1vW7EW2zxMtQPKPk9+O9v+QvZ53MhuHNqPiIidXO6kjoiIRkkQERHRaKdPEJIukrRB0o96HctEkDRL0jcl/VjSKklv6nVMbZO0m6TvS/ph2ef39TqmiSJpiqQbJH2517FMBElrJd0s6UZJ/b2OZyJI2lvS5yX9pPy/fsa4bXtnPwch6UhgI9Ud3U/pdTxtK3eqP9r2DyTtCawETrB9S49Da40kAdNsb5T0EODbwJvK3fuTmqS3APOAvWw/v9fxtE3SWmCe7Z3mRjlJFwPX276wXPW5u+27x2PbO/0RhO3rgDt7HcdEsf0b2z8oy/cCP6a6c33SKlO5bCyrDymvSf+XkaSZwPHAhb2OJdohaS/gSODjALbvG6/kAEkQOzVJs4GnA9/rbSTtK0MtNwIbgGtsT/p9Bj4EvA14sNeBTCADV0taWabhmeweDwwCnyhDiRdKmjZeG0+C2ElJ2gP4AvBm2/f0Op622X7A9tOo7so/VNKkHk6U9Hxgg+2VvY5lgh1hey7VTNBnliHkyWxXYC7wMdtPB34PjNvjEZIgdkJlHP4LwGdtX97reCZSOfz+FnBsj0Np2xHAC8uY/FLg2ZI+09uQ2md7fXnfAFxBNTP0ZDYADNSOiD9PlTDGRRLETqacsP048GPbH+x1PBNBUp+kvcvyw4DnAD/pbVTtsv0O2zNtz6aaquYbtl/e47BaJWlaufCCMsxyDDCpr060fRuwTtITS9HfAON2wUmbk/XtECR9DngWMF3SAPBe2x/vbVStOgI4Fbi5jMkDvNP28h7G1LZHAxeXB1HtAlxme6e47HMn8yjgiupvIHYFLrH91d6GNCHeAHy2XMG0hjJl0XjY6S9zjYiIZhliioiIRkkQERHRKAkiIiIaJUFERESjJIiIiGiUBBHRQ2X20em9jiOiSRJExDiTtNPfXxSTQ/4hR4ySpHcDpwDrgNuppkx/PvAdqhsRl0n6KfAuYCpwB3CK7d9K2hf4HNAHfB9QbbsvB95Y+nwPeL3tByZqvyI65QgiYhQkzQNeTDUL7t9SPWthyN62j7L9L1TPnDi8TKC2lGpWVYD3At8u5cuAx5btPgl4KdVkc08DHqBKQhE9kyOIiNF5JvAftv8IIOlLtbpLa8szgUvLA5qmAr8o5UdSJRZsXynprlL+N8D/AFaUqSIeRjU1eUTPJEFEjI62Uvf72vK/Ah+0vUzSs4Cza3VN89sIuNj2O7Y5wohxkiGmiNH5NvCC8pzrPaie2Nbk4cCvy/Lf18qvowwdSZoP7FPKvw68RNIjS90jJD1uvIOPGI0kiIhRsL2C6tzBD4HLgX7gdw1Nzwb+XdL1VCeyh7wPOFLSD6imo/5V2e4tVCe1r5Z0E3AN1Sy0ET2T2VwjRknSHrY3Stqd6ohg4dBzviMmk5yDiBi9JZIOAnajOm+Q5BCTUo4gIiKiUc5BREREoySIiIholAQRERGNkiAiIqJREkRERDT6/z3ppz2Uckp2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "student_count = 10000\n",
+    "grades = random.normal(loc=3, scale=1, size=student_count)\n",
+    "\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "ax1.set_title('Probability distribution grades')\n",
+    "ax1.set_xlabel(\"grade\");\n",
+    "ax1.set_ylabel(\"probability\");\n",
+    "ax1.hist(grades, range=(1,6), bins=6, density=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_Hypothesis_testing/docstrings_example.py b/11_Hypothesis_testing/docstrings_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..d94f7368bf2281c5210f33562862d4be7ebe0dce
--- /dev/null
+++ b/11_Hypothesis_testing/docstrings_example.py
@@ -0,0 +1,67 @@
+from skimage.io import imread
+from skimage import filters
+from skimage import measure
+
+
+def analyse_image(filename, gaussian_blur_sigma, threshold=None):
+    """
+    This function analyses an image by blurring it and applying a threshold.
+
+    Parameters
+    ----------
+    filename
+        Image file to open and analyse
+
+    gaussian_blur_sigma
+        Sigma of a Gaussian blur filter kernel to be
+        applied to the image before thresholding
+
+    threshold
+        Optional, the grey-value threshold to
+        differentiate foreground and background
+
+    Returns
+    -------
+    count
+        Number of objects in the image
+
+    """
+
+    # load image
+    image = imread(filename);
+
+    # Gaussian blur
+    gaussian_blurred_image = filters.gaussian(image, 5)
+
+    # thresholding
+    if threshold is None:
+        threshold = filters.threshold_otsu(gaussian_blurred_image)
+    thresholded_image = gaussian_blurred_image >= threshold
+
+    # run connected components analysis
+    label_image = measure.label(thresholded_image)
+
+    # analyse objects
+    table = measure.regionprops_table(label_image)
+
+    return len(table["label"])
+
+
+analyse_image("blobs.tif", 2)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/11_Hypothesis_testing/math_library.py b/11_Hypothesis_testing/math_library.py
new file mode 100644
index 0000000000000000000000000000000000000000..11de9ba3eec6d8a373e6f41ec6cc1e47b3befd00
--- /dev/null
+++ b/11_Hypothesis_testing/math_library.py
@@ -0,0 +1,5 @@
+def add(a, b):
+    return a + b;
+
+def subtract(a, b):
+    return a - b;
\ No newline at end of file
diff --git a/11_Hypothesis_testing/my_statistics_functions.py b/11_Hypothesis_testing/my_statistics_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..7049eabd39c3a7c5b3bc514eb4dc7d8943b5aa50
--- /dev/null
+++ b/11_Hypothesis_testing/my_statistics_functions.py
@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+
+def draw_curation_time_histogram(data, description):
+    fig, ax = plt.subplots()
+    ax.hist(data, bins=10)
+    ax.set_title('Curation time of ' + str(len(data)) + ' ' + description)
+    ax.set_ylabel("count")
+    ax.set_xlabel("Curation time / days")
+    plt.show()
+
+
+test_data = [1,1, 3,3, 5,6,7, 9,9]
+
+draw_curation_time_histogram(test_data, "examples")
+
+
diff --git a/11_Hypothesis_testing/patients.py b/11_Hypothesis_testing/patients.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a8c5815cbef6f0d32b4da1c95dfca3981dd5d7
--- /dev/null
+++ b/11_Hypothesis_testing/patients.py
@@ -0,0 +1,12 @@
+
+from numpy import random
+
+def placebo_group(number_of_patients):
+    # generate random numbers following a normal distribution
+    x = random.normal(loc=7, scale=2, size=number_of_patients)
+    return x
+
+def treatment_group(number_of_patients):
+    # generate random numbers following a normal distribution
+    x = random.normal(loc=7, scale=2, size=number_of_patients)
+    return x
diff --git a/11_Hypothesis_testing/tomatoes.py b/11_Hypothesis_testing/tomatoes.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9768fddbf17ea5949ea525aa3760afe9ef96aaf
--- /dev/null
+++ b/11_Hypothesis_testing/tomatoes.py
@@ -0,0 +1,7 @@
+
+from numpy import random
+
+def ripen(number_of_tomatoes):
+    # generate random numbers following a normal distribution
+    x = random.normal(loc=24.5, scale=2, size=number_of_tomatoes)
+    return x
\ No newline at end of file