diff --git a/run_simulation_80power.ipynb b/run_simulation_80power.ipynb
new file mode 100644
index 0000000..31a30bc
--- /dev/null
+++ b/run_simulation_80power.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "71e121d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import simulation_tools as sim\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fb17ca19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to a .csv file with connectomes in upper triangular form\n",
+    "path_conn = \"/home/neuromod/ad_sz/data/abide/abide1_2_controls_concat.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8b6c3bf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load control connectomes from ABIDE\n",
+    "conn_df = pd.read_csv(path_conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5df7cb69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a range of N values\n",
+    "N_values = range(400, 501, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b4565ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation ran for N=400.\n",
+      "Simulation ran for N=410.\n",
+      "Simulation ran for N=420.\n",
+      "Simulation ran for N=430.\n",
+      "Simulation ran for N=440.\n",
+      "Simulation ran for N=450.\n",
+      "Simulation ran for N=460.\n",
+      "Simulation ran for N=470.\n",
+      "Simulation ran for N=480.\n",
+      "Simulation ran for N=490.\n",
+      "Simulation ran for N=500.\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_list = []\n",
+    "# Loop through the values of N and run simulation with specififed parameters\n",
+    "for N in N_values:\n",
+    "    result = sim.run_multiple_simulation(conn_df, N=N, pi=0.20, d=0.3, q=0.1, num_sample=100)\n",
+    "    print(f\"Simulation ran for N={N}.\")\n",
+    "    result_list.append(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ebf2916e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=400: 0.71, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=410: 0.72, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=420: 0.75, with mean specificity of 0.99.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=430: 0.75, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=440: 0.78, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=450: 0.79, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=460: 0.8, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=470: 0.81, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=480: 0.81, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=490: 0.83, with mean specificity of 0.98.\n",
+      "Estimated mean sensitivity to detect d=0.3, with pi=0.2%, q=0.1 and N=500: 0.84, with mean specificity of 0.98.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for result in result_list:\n",
+    "    print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd6f422f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}