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    "colab": {
      "provenance": []
    },
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      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
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              "widget-interact"
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            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_c7dca11ee80d4dda989ea34caac55d67",
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        },
        "c7dca11ee80d4dda989ea34caac55d67": {
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "Valor p",
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            "max": 1,
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            "value": 0.04
          }
        },
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
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        "07a7cae5347244bc9c8d75f1a928dca7": {
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            "_view_name": "OutputView",
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            "msg_id": "",
            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "=== Simulador de Significancia Estadística ===\n",
                  "Valor p: 0.04\n",
                  "Nivel de significancia α: 0.05\n",
                  "Decisión: Rechazar H0\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 800x200 with 1 Axes>",
                  "image/png": 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/z6GFhQXs7Ozg7++vsF5EusRrKqlc8fHxgaenJzZv3ozvv/8emzdvhhBCHjaLIpFIsH37dpw6dQp79+7FoUOHEBoaisWLF+PUqVOwsLAo8ghMXl5eoWG9evXCyZMnMWHCBDRs2BAWFhbIz89H27ZtkZ+fX6Z1CwkJwf3793HmzBlYWloqjBs1ahTWrVuHMWPGwM/PD1ZWVpBIJOjTp0+Zl6eKogK8EEIt7efn58Pb2xs//fST0vFvX3/5PpaWlmjdujVat24NY2NjRERE4PTp0/J/bFWl6W1REnPmzMEPP/yA0NBQzJo1C5UrV4aBgQHGjBmjdP/QVM23bt1Cq1at4OnpiZ9++gkuLi6QSqU4cOAAlixZorZ91cvLC9evX8e+fftw8OBB7NixAytWrMDUqVMxY8aMclHj4sWLERISgj/++AOHDx/G119/jblz5+LUqVOoVq2aWpbxtry8PLRu3RrPnj3DpEmT4OnpCXNzc9y7dw8hISE6+TtB9C6GSip3goKC8MMPP+DSpUvYtGkTatWqhU8++aRE8zZt2hRNmzbF7NmzsWnTJgQFBWHLli0YMmSI/OjDuw9LLjhiUeD58+c4cuQIZsyYgalTp8qH37hxo8zrNG/ePOzevRs7d+6Ep6dnofHbt2/HwIEDsXjxYvmwzMzMQrV6eHjg8uXLpVp29erVAQDXr18vNO7atWuwtbWFubl5qdosbjk3btyQn8YG3pzOT0pKwkcffSQf5uHhgYsXL6JVq1ZqfWNL48aNERERgQcPHhRbo7I7oZUN0zQ7OztYWlq+t0+3b9+Ozz77DL/99pvC8BcvXpTp0TwFN4xcvnwZAQEBJZ5v7969yMrKwp49exSOhr7vcoWyMDc3R+/evdG7d29kZ2eje/fumD17Nr777juYmJgUud+UtMa399e3j6Q/efKkxEehvb294e3tjSlTpuDkyZNo3rw5Vq1ahR9//BHA+y8lKPB2fxR1xDw+Ph7//PMPIiIiFB67FhkZWaJlEGkDT39TuVNwVHLq1KmIi4t771FK4E0QfPcoTMOGDQFAfgq8evXqMDQ0xF9//aUw3YoVKxQ+Fxzlebe9pUuXlngd3hYVFYUpU6Zg8uTJhR6C/PYy311eeHh4oaOoPXr0wMWLF7Fr165CbRR1FMrR0RENGzZERESEQki9fPkyDh8+jPbt25duhYrQuHFj2NnZYdWqVQp3z65fv75QOO7Vqxfu3buH1atXF2onIyOj0On4t6WnpyM2NlbpuD///BOA8lP9AODk5IT69evj999/V7gj/7///S/i4+OLXKamFLy+ce/evfj7778LjS/oU2X7x7Zt23Dv3r0yLbdRo0Zwc3PD0qVLC/VNcUczlX03Xr58qXA9pDq8++guqVSKunXrQgghv2a64D9C79Zf0hoDAgJgbGyM8PBwhWlL8j1PTU1Fbm6uwjBvb28YGBgoXHJjbm5eojf+tGnTBpUqVcLcuXMLXbrx9j7w7noJIRQeAUWkazxSSeWOm5sbmjVrhj/++AMAShQqIyIisGLFCnTr1g0eHh5IS0vD6tWrYWlpKQ9NVlZW6NmzJ8LDwyGRSODh4YF9+/YVun7P0tISn376KRYsWICcnBw4Ozvj8OHDSEpKKtP69O3bF3Z2dqhVqxb+93//V2Fc69atUbVqVXTs2BEbNmyAlZUV6tati9jYWERFRckfOVRgwoQJ2L59O3r27InQ0FD4+Pjg2bNn2LNnD1atWqVwNPBtCxcuRLt27eDn54fBgwfLHylkZWWltvdQGxsb48cff8SwYcPw+eefo3fv3khKSsK6desKXVM5YMAAbN26FcOHD8exY8fQvHlz5OXl4dq1a9i6dSsOHTqk9MYV4E2obNasGZo2bYq2bdvCxcUFL168wO7duxETE4OuXbsW+07zOXPmoEuXLmjevDkGDRqE58+f4+eff0b9+vULPfpJG+bMmYPDhw/D399f/nilBw8eYNu2bTh+/Disra3RsWNHzJw5E4MGDUKzZs0QHx+PjRs3FtquJWVgYICVK1eiU6dOaNiwIQYNGgRHR0dcu3YNV65cwaFDh5TO16ZNG0ilUnTq1AnDhg3Dq1evsHr1atjb2xd5dFiZ7du3K32jTsH3oU2bNnBwcEDz5s1RtWpVJCQk4Oeff0aHDh1QqVIlAG8ulQGAyZMno0+fPjA2NkanTp1KXKOdnR3Gjx+PuXPnomPHjmjfvj0uXLiAP//8871Hf48ePYqRI0eiZ8+eqF27NnJzc7FhwwYYGhqiR48e8ul8fHwQFRWFn376CU5OTnBzc0OTJk0KtWdpaYklS5ZgyJAh+OSTT9CvXz/Y2Njg4sWLSE9PR0REBDw9PeHh4YHx48fj3r17sLS0xI4dO9RybS+R2mj5bnOiElm+fLkAIHx9fZWOf/eRHOfPnxd9+/YVrq6uQiaTCXt7e9GxY0fx999/K8z35MkT0aNHD2FmZiZsbGzEsGHDxOXLlws9dubu3buiW7duwtraWlhZWYmePXuK+/fvF3ocTEkeKYQiHqGCtx5R8vz5czFo0CBha2srLCwsRGBgoLh27ZqoXr16oUeSPH36VIwcOVI4OzsLqVQqqlWrJgYOHChSUlKEEMofoyOEEFFRUaJ58+bC1NRUWFpaik6dOomrV68qTFPwyJcnT54Uu72Ls2LFCuHm5iZkMplo3Lix+Ouvv5Q+Zik7O1vMnz9f1KtXT8hkMmFjYyN8fHzEjBkzxMuXL4tsPycnR6xevVp07dpVVK9eXchkMmFmZiY+/vhjsXDhQoVH6RS1LbZs2SI8PT2FTCYT9evXF3v27BE9evQQnp6eheZduHBhoRre3Q9UeaSQEELcuXNHBAcHCzs7OyGTyYS7u7sICwuTr0tmZqb45ptvhKOjozA1NRXNmzcXsbGxhbZrwaNv3n3UTVHb4fjx46J169aiUqVKwtzcXDRo0ECEh4cXu1579uwRDRo0ECYmJqJGjRpi/vz5Yu3atSXaP4p7pNDb34dffvlFfPrpp6JKlSpCJpMJDw8PMWHChEL7xaxZs4Szs7MwMDBQWH5Ja8zLyxMzZsyQb9eWLVuKy5cvF/revftIocTERBEaGio8PDyEiYmJqFy5svjss89EVFSUQn3Xrl0Tn376qTA1NVV4TFFR36c9e/aIZs2ayb+jvr6+YvPmzfLxV69eFQEBAcLCwkLY2tqKL7/8Uly8eFFp3xLpgkQILV5tTkRUTjVs2BB2dna8Ro2IqIx4TSURVSg5OTmFroeLjo7GxYsXFV6vSUREpcMjlURUody+fRsBAQHo378/nJyccO3aNaxatQpWVla4fPlyoetYiYioZHijDhFVKDY2NvDx8cGaNWvw5MkTmJubo0OHDpg3bx4DJRGRCnikkoiIiIhUxmsqiYiIiEhlDJVEREREpDKGSiIiIiJSmdpv1MnPz8f9+/dRqVIltb7Tl4iIiIjUQwiBtLQ0ODk5wcBAPccY1R4q79+/DxcXF3U3S0RERERq9u+//6JatWpqaUvtobLgvaz//vsvLC0t1d08EREREakoNTUVLi4u8tymDmoPlQWnvC0tLbUaKvPy8hATEwMAaNGiBQwNDbW2bCIiIqIPkTovVdSbh59nZmbis88+AwC8evUK5ubmOq6IiIiIqOLg3d9EREREpDKGSiIiIiJSmcZOf8fFxcHCwkJTzReSkZGhtWURERFVREII5ObmIi8vT9el0HsYGhrCyMhIq4931Fio9Pf311TT7/Xvv//C09NTZ8snIiLSN9nZ2Xjw4AHS09N1XQqVkJmZGRwdHSGVSrWyPI2FyuGzFsKjrremmi8kOzMTk4O6AgCePn2qteUSERHpu/z8fCQlJcHQ0BBOTk6QSqV8wUk5JoRAdnY2njx5gqSkJNSqVUttDzgvjsZCpXMNd7jXa6Cp5gvJ5P+ciIiINCI7Oxv5+flwcXGBmZmZrsuhEjA1NYWxsTHu3LmD7OxsmJiYaHyZenOjjqGREToMGAwAMDLSmyclERERlRvaONpF6qPt/tKbvcNYKkXLrj3f/G5srONqiIiIiCoWvQmVRERERKQ7enOeOC8vD//evC7/nYiIiDQrOTkZKSkpWluera0tXF1dtba8skpOTsaIESNw7NgxWFhYYODAgZg7d26xl+c9e/YMo0aNwt69e2FgYIAePXpg2bJl8scz3r59G25uboXmi42NRdOmTTW2LqWhN6EyJysL/zNpNIA3FxQTERGR5iQnJ8PLy0urjxgyMzNDQkJCuQ6WeXl56NChAxwcHHDy5Ek8ePAAwcHBMDY2xpw5c4qcLygoCA8ePEBkZCRycnIwaNAgDB06FJs2bVKYLioqCvXq1ZN/rlKlisbWpbT0JlQSERGR9qSkpCA9PR2jF/6Mau41Nb68u4k3sWzCSKSkpJQ4VL569QqjRo3C9u3bYWZmhvHjx6Nfv36oVasWHj9+rJGXtBw+fBhXr15FVFQUqlatioYNG2LWrFmYNGkSpk+frvSZkQkJCTh48CDOnj2Lxo0bAwDCw8PRvn17LFq0CE5OTvJpq1SpAgcHB7XXrQ4MlURERFRm1dxravURgqUREhKC+Ph4REdH49GjR+jevTsuX76MgICAYgPl+8Jm//79sWrVKqXjYmNj4e3tjapVq8qHBQYGYsSIEbhy5Qo+/vhjpfNYW1vLAyUABAQEwMDAAKdPn0a3bt3kwzt37ozMzEzUrl0bEydOROfOnYutVZsYKomIiEjvpKSkYOfOndi4cSN8fHwAAN26dcPvv/+O3377rdh54+Liih1vaWlZ5LiHDx8qBEoA8s8PHz4sch57e3uFYUZGRqhcubJ8HgsLCyxevBjNmzeHgYEBduzYga5du2L37t3lJlgyVBIREZHeuXnzJoQQ8PPzkw/z9fXFtm3b3hvCatbU/On80rK1tcW4cePknz/55BPcv38fCxcuLDehko8UIiIiIr0jk8kAQOEaRjs7O9SuXRu2trbFzmthYVHsz/Dhw4uc18HBAY8ePVIYVvC5qGshHRwc8PjxY4Vhubm5ePbsWbHXTzZp0gQ3b94sdl20iUcqiYiISO+4ubnBwMAAN27ckN/osmfPHiQnJ0MIUey7y1U5/e3n54fZs2fj8ePH8lPakZGRsLS0RN26dYuc58WLFzh37pz8VP3Ro0eRn5+PJk2aFFuno6NjsbVqk96ESkMjI7TuFYTIrRv5mkYiIqIKztraGt27d8fs2bPh6+uLf/75BwcPHoSpqSmOHj2KVq1aFTmvKqe/27Rpg7p162LAgAFYsGABHj58iClTpiAsLEx+9PTMmTMIDg7GkSNH4OzsDC8vL7Rt2xZffvklVq1ahZycHIwcORJ9+vSRB+KIiAhIpVL5jT47d+7E2rVrsWbNmjLXqm56k76MpVK06T0AkVs38jWNREREWnI3UTunX8uynOXLl2PIkCFwdnaGoaEhli5dCplMhqCgIMyePRuDBw9We52GhobYt28fRowYAT8/P5ibm2PgwIGYOXOmfJr09HRcv34dOTk58mEbN27EyJEj0apVK/nDz//nf/5Hoe1Zs2bhzp07MDIygqenJ/7v//4PX3zxhdrXoawkQgihzgZTU1NhZWWFWRt2oO4nfu+fQY0Sr1zChB5tce7cOTRq1EiryyYiItJXmZmZSEpKgpubG0xMTADw4ecfAmX9VqAgr718+bLY0/mloTdHKvPz8/Ew+bb8dyIiItIcV1dXJCQk8DWNJKc3oTI7MxOLx765GysrK0vH1RAREek/V1dXhjyS4yOFiIiIiEhlDJVEREREpDKGSiIiIiJSGUMlEREREamMoZKIiIiIVMZQSUREREQq05tQaWhkBP/OPQCAr2kkIiIi0jK9SV/GUik6DvwS/92zg69pJCIi0oLk5GQ+/JzkNBYq792+BRMzc001j0o2lWHnVE1j7RMREVHRkpOT4VWnDtIzM7W2TDMTEyRcv17ug2V0dDTGjRuHK1euwMXFBVOmTEFISEix81y6dAlhYWE4e/Ys7OzsMGrUKEycOFHptFu2bEHfvn3RpUsX7N69W/0rUEYaC5WrflC+IdTFRCrD0oMx8mCZn5+PZ48fyn8nIiIizUlJSUF6Zib+F4CXFpaXAKB/ZiZSUlLKdahMSkpChw4dMHz4cGzcuBFHjhzBkCFD4OjoiMDAQKXzpKamok2bNggICMCqVasQHx+P0NBQWFtbY+jQoQrT3r59G+PHj0eLFi20sTqlorFQ+SsAHw21nQCgf3YW0p4/k4fK7MxMzB0RAoCvaSQiItIWLwCNdF1EEVq2bIn69esDADZs2ABjY2OMGDECM2fOhEQi0cgyV61aBTc3NyxevBgA4OXlhePHj2PJkiVFhsqNGzciOzsba9euhVQqRb169RAXF4effvpJIVTm5eUhKCgIM2bMQExMDF68eKGRdSgrjYXKOii/OxkRERFVDBERERg8eDDOnDmDv//+G0OHDoWrqyu+/PJLpdPHxMSgXbt2xbb5yy+/ICgoSOm42NhYBAQEKAwLDAzEmDFjimwvNjYWn376KaRSqcI88+fPx/Pnz2FjYwMAmDlzJuzt7TF48GDExMQUW6Mu6M2NOkRERETvcnFxwZIlSyCRSFCnTh3Ex8djyZIlRYbKxo0bIy4urtg2q1atWuS4hw8fFhpftWpVpKamIiMjA6ampkrncXNzU7qMhw8fwsbGBsePH8dvv/323tp0iaGSiIiI9FbTpk0VTnX7+flh8eLFyMvLg6GhYaHpTU1NUbNmTW2W+F5paWkYMGAAVq9eDVtbW12XUySGSiIiIqL/T9XT3w4ODnj06JHCsEePHsHS0lLpUcri5ikYd+vWLdy+fRudOnWSjy+4KdnIyAjXr1+Hh4dH8SumBQyVREREpLdOnz6t8PnUqVOoVauW0qOUgOqnv/38/HDgwAGFYZGRkfDz8yt2nsmTJyMnJ0f+rO3IyEjUqVMHNjY2MDU1RXx8vMI8U6ZMQVpaGpYtWwYXF5di69UWhkoiIiLSW8nJyRg3bhyGDRuG8+fPIzw8XH5ntjKqnv4ePnw4fv75Z0ycOBGhoaE4evQotm7div3798un+fnnn7Fr1y4cOXIEANCvXz/MmDEDgwcPxqRJk3D58mUsW7YMS5YsAQCYmJjI72IvYG1tDQCFhuuS3oRKQyND+LXtiNiD+4r83wcRERGpV0I5X05wcDAyMjLg6+sLQ0NDjB49utCzH9XJzc0N+/fvx9ixY7Fs2TJUq1YNa9asUXicUEpKCm7duiX/bGVlhcOHDyMsLAw+Pj6wtbXF1KlTNVqnJuhNqDSWytD9y5GIPbhP4ZZ8IiIiUj9bW1uYmZigv5bfqFPaG1WMjY2xdOlSrFy5UkNVFdayZUtcuHChyPHTp0/H9OnTFYY1aNCgVI8JWr9+fRmr0xy9CZVERESkPa6urki4fp3v/iY5vQmVQgi8evlC/jsRERFplqurK0MeyelNqMzKyMCM0D4AgEwtHoonIiKi8ik6OlrXJVQoBrougIiIiIg+fAyVRERERKQyhkoiIiIqEd6z8GHRdn8xVBIREVGxCt7ykp6eruNKqDQK+qug/zRNb27UISIiIs0wNDSEtbU1Hj9+DAAwMzODRCLRcVVUFCEE0tPT8fjxY1hbW2vtpTAMlURERPReDg4OACAPllT+WVtby/tNG/QmVBoaGcKnZQDORUfxNY1ERERqJpFI4OjoCHt7e+Tk5Oi6HHoPY2NjrechvQmVxlIZ+owaj3PRUXxNIxERkYYYGhry4A0pxRt1iIiIiEhlehMqhRDI/v9v0uEjD4iIiIi0S29CZVZGBiYHdQXA1zQSERERaZvehEoiIiIi0h2GSiIiIiJSGUMlEREREamMoZKIiIiIVMZQSUREREQqY6gkIiIiIpXpTag0MDRAA7//vPndQG9Wi4iIiOiDoDfpSyozwYDxUwAAMplMx9UQERERVSx6EyqJiIiISHcYKomIiIhIZXoTKjPT0zGhR1sAQEZGho6rISIiIqpY9CZUEhEREZHuMFQSERERkcoYKomIiIhIZQyVRERERKQyhkoiIiIiUhlDJRERERGpTG9CpYGhATwbffLmd76mkYiIiEir9CZ9SWUmGDx5FgC+ppGIiIhI2/QmVBIRERGR7jBUEhEREZHK9CZUZqan4/t+XQDwNY1ERERE2qY3oRIAcrKydF0CERERUYWkV6GSiIiIiHSDoZKIiIiIVMZQSUREREQqY6gkIiIiIpUxVBIRERGRyvQmVEoMJHCv5/3md4lEx9UQERERVSx6EyplJqYYMXMhAMDExETH1RARERFVLHoTKomIiIhIdxgqiYiIiEhlehMqM9PTMX1QbwB8TSMRERGRtulNqASA16kvdV0CERERUYWkV6GSiIiIiHSDoZKIiIiIVMZQSUREREQqY6gkIiIiIpUxVBIRERGRyvQmVEoMJKjmUevN73xNIxEREZFW6U2olJmYYvSCcAB8TSMRERGRtulNqCQiIiIi3WGoJCIiIiKV6U2ozMpIx5zhwQD4mkYiIiIibdObUCkE8PzJY12XQURERFQh6U2oJCIiIiLdYagkIiIiIpUxVBIRERGRyhgqiYiIiEhlDJVEREREpDK9CZUSCVC1mquuyyAiIiKqkPQmVMpMzTB+2a8AAFNTUx1XQ0RERFSx6E2oJCIiIiLdYagkIiIiIpXpTajMykjHotFDAfA1jURERETapjehUgjg0d1kXZdBREREVCHpTagkIiIiIt1hqCQiIiIilTFUEhEREZHKjDTV8HUAFhpqO0FD7RIRERFR2WgsVA7VVMP/n4lUhko2lTW8FCIiIiIqCY2FyuGzFsCjbgNNNY9KNpVh51RN/lkiAWzs7PH8yWONLZOIiIiIlNNYqHSu4QH3epoLle+SmZrh+1W/Y0KPtnxNIxEREZGW8UYdIiIiIlIZQyURERERqUxvQmVWZgaWTRwFAMjMzNRxNUREREQVi96ESpEvcPfWjTe/C6HjaoiIiIgqFr0JlURERESkOwyVRERERKQyhkoiIiIiUhlDJRERERGpjKGSiIiIiFSmV6HS3NJK1yUQERERVUh6EypNzMwwfd3/AQBf00hERESkZXoTKomIiIhIdxgqiYiIiEhlehMqszIzsHLqBAB8TSMRERGRtulNqBT5AolX4t/8ztc0EhEREWmV3oRKIiIiItIdhkoiIiIiUhlDJRERERGpjKGSiIiIiFTGUElEREREKtOrUGksk+m6BCIiIqIKSW9CpYmZGeZs+gMAX9NIREREpG16EyqJiIiISHcYKomIiIhIZUaaavje7USYmJlrqvlCcrKz8euM7wAAWVlZWlsuEREREWkwVK76YYKmmn4vGxsbnS2biIiIqCLSWKj873//CwsLC001X0hGRgb+85//AABcXFy0tlwiIiIi0mCobNiwISwtLTXVfCGvX7/W2rKIiIiISBFv1CEiIiIilTFUEhEREZHKGCqJiIiISGVqv6ZSCAEASE1NVXfTxXr7msrU1FTk5eVpdflEREREH4qCnFaQ29RB7aHy6dOnAHR7B7aTk5POlk1ERET0oXj69CmsrKzU0pbaQ2XlypUBAMnJyWorksqv1NRUuLi44N9//9Xq3f6kG+zvioX9XbGwvyuWly9fwtXVVZ7b1EHtodLA4M1lmlZWVtwpKxBLS0v2dwXC/q5Y2N8VC/u7YinIbWppS20tEREREVGFxVBJRERERCpTe6iUyWSYNm0aZDKZupumcoj9XbGwvysW9nfFwv6uWDTR3xKhznvJiYiIiKhC4ulvIiIiIlIZQyURERERqYyhkoiIiIhUxlBJRERERCorU6hcvnw5atSoARMTEzRp0gRnzpwpdvpt27bB09MTJiYm8Pb2xoEDB8pULOlGafp79erVaNGiBWxsbGBjY4OAgID37h9UvpT2+11gy5YtkEgk6Nq1q2YLJLUqbX+/ePECYWFhcHR0hEwmQ+3atfk3/QNS2v5eunQp6tSpA1NTU7i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                "name": "stdout",
                "text": [
                  "\n",
                  "💡 Ejemplo financiero:\n",
                  "- Si estamos comparando el rendimiento de dos portafolios y p < α,\n",
                  "  podemos concluir que la diferencia es estadísticamente significativa.\n",
                  "- Si p >= α, la diferencia no es significativa; no hay evidencia suficiente para afirmar cambios en rendimiento.\n"
                ]
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              "<function __main__.visualizador_significancia(p_valor=0.04, alpha=0.05)>"
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "        overflow-x: auto;\n",
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              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>visualizador_significancia</b><br/>def visualizador_significancia(p_valor=0.04, alpha=0.05)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_5781/168128281.py</a>Simula la decisión estadística según el valor p y el nivel de significancia.\n",
              "\n",
              "Parámetros:\n",
              "- p_valor: valor p obtenido en la prueba estadística\n",
              "- alpha: nivel de significancia</pre></div>"
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      "source": [
        "# 📦 Librerías necesarias\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact, FloatSlider\n",
        "import numpy as np\n",
        "\n",
        "# 🔹 Función para visualizar significancia estadística\n",
        "def visualizador_significancia(p_valor=0.04, alpha=0.05):\n",
        "    \"\"\"\n",
        "    Simula la decisión estadística según el valor p y el nivel de significancia.\n",
        "\n",
        "    Parámetros:\n",
        "    - p_valor: valor p obtenido en la prueba estadística\n",
        "    - alpha: nivel de significancia\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Decisión según p-valor y alpha\n",
        "    if p_valor < alpha:\n",
        "        decision = \"Rechazar H0\"\n",
        "        color = 'red'\n",
        "    else:\n",
        "        decision = \"No rechazar H0\"\n",
        "        color = 'green'\n",
        "\n",
        "    # 🔹 Mostrar información textual\n",
        "    print(\"=== Simulador de Significancia Estadística ===\")\n",
        "    print(f\"Valor p: {p_valor}\")\n",
        "    print(f\"Nivel de significancia α: {alpha}\")\n",
        "    print(f\"Decisión: {decision}\")\n",
        "\n",
        "    # 🔹 Gráfica visual\n",
        "    fig, ax = plt.subplots(figsize=(8, 2))\n",
        "\n",
        "    # Barras representando alpha y p\n",
        "    ax.barh(0, alpha, color='lightblue', edgecolor='black', height=0.5, label=f'α = {alpha}')\n",
        "    ax.barh(0, p_valor, color=color, edgecolor='black', height=0.3, label=f'p = {p_valor}')\n",
        "\n",
        "    ax.set_xlim(0, 1)\n",
        "    ax.set_yticks([])\n",
        "    ax.set_xlabel('Valores')\n",
        "    ax.set_title('Visualización de Significancia Estadística')\n",
        "    ax.legend()\n",
        "\n",
        "    # 🔹 Línea vertical de decisión\n",
        "    ax.axvline(x=alpha, color='black', linestyle='--')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "    # 🔹 Ejemplo práctico financiero\n",
        "    print(\"\\n💡 Ejemplo financiero:\")\n",
        "    print(\"- Si estamos comparando el rendimiento de dos portafolios y p < α,\")\n",
        "    print(\"  podemos concluir que la diferencia es estadísticamente significativa.\")\n",
        "    print(\"- Si p >= α, la diferencia no es significativa; no hay evidencia suficiente para afirmar cambios en rendimiento.\")\n",
        "\n",
        "# 🔹 Interactividad con sliders\n",
        "interact(\n",
        "    visualizador_significancia,\n",
        "    p_valor=FloatSlider(value=0.04, min=0.0, max=1.0, step=0.01, description='Valor p'),\n",
        "    alpha=FloatSlider(value=0.05, min=0.01, max=0.10, step=0.005, description='α')\n",
        ")"
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