diff --git a/examples/Analysis.ipynb b/examples/Analysis.ipynb
index a4e9483..04d822d 100644
--- a/examples/Analysis.ipynb
+++ b/examples/Analysis.ipynb
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 346,
+   "execution_count": 3,
    "id": "d5881554-9c21-4f8b-a9a5-0ea47e671f4a",
    "metadata": {},
    "outputs": [],
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 347,
+   "execution_count": 4,
    "id": "fdb09081-c75d-4f4b-ba5b-01199a6151fc",
    "metadata": {},
    "outputs": [
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 348,
+   "execution_count": 5,
    "id": "f47df37a-1d7f-46a9-a439-3599929e1e9c",
    "metadata": {},
    "outputs": [
@@ -284,211 +284,135 @@
     "print(meas_dataset)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "da895fbe",
+   "metadata": {},
+   "source": [
+    "# Validation"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "87c93e9c-4e51-4818-8d87-381a83506c69",
+   "id": "0385f66c-26aa-4d04-b5c8-0305903c6e22",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def asimov_dataset():\n",
-    "    from titrate.datasets import AsimovMapDataset\n",
-    "    from titrate.utils import copy_models_to_dataset\n",
-    "\n",
-    "    maker = MapDatasetMaker(selection=[\"exposure\", \"background\", \"psf\", \"edisp\"])\n",
-    "    maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=4.0 * u.deg)\n",
-    "\n",
-    "    empty_asimov = AsimovMapDataset.create(\n",
-    "        geometry3d(),\n",
-    "        energy_axis_true=energy_axes()[\"true\"],\n",
-    "        migra_axis=energy_axes()[\"migra\"],\n",
-    "        name=\"asimov\",\n",
-    "    )\n",
-    "\n",
-    "    asimov_dataset = maker.run(empty_asimov, observation())\n",
-    "    asimov_dataset = maker_safe_mask.run(asimov_dataset, observation())\n",
-    "\n",
-    "    copy_models_to_dataset(dm_models(), asimov_dataset)\n",
-    "\n",
-    "    asimov_dataset.fake()\n",
-    "\n",
-    "    return asimov_dataset"
+    "from titrate.validation import AsymptoticValidator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e82fd440",
+   "metadata": {},
+   "source": [
+    "## QMuTestStatistic"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "186e9cc5-87fd-4c25-9c6a-01d0bf46ae65",
+   "id": "a0d66aaf-be8a-4f9f-81b8-77461b7cc43c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
-      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/gammapy/data/observations.py:226: GammapyDeprecationWarning: Pointing will be required to be provided as FixedPointingInfo\n",
-      "  warnings.warn(\n",
-      "WARNING: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
-      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/gammapy/data/observations.py:226: GammapyDeprecationWarning: Pointing will be required to be provided as FixedPointingInfo\n",
-      "  warnings.warn(\n",
-      "WARNING: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
-      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/gammapy/data/observations.py:226: GammapyDeprecationWarning: Pointing will be required to be provided as FixedPointingInfo\n",
-      "  warnings.warn(\n",
-      "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
-      "WARNING: UnitsWarning: '1/s/MeV/sr' did not parse as fits unit: Numeric factor not supported by FITS If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n",
-      "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n",
-      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/gammapy/data/observations.py:226: GammapyDeprecationWarning: Pointing will be required to be provided as FixedPointingInfo\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "asi_dataset = asimov_dataset()"
+    "validator = AsymptoticValidator(meas_dataset, statistic='qmu', poi_name='scale')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "e3bab885-9e11-467a-b487-f3e9484503e0",
+   "execution_count": 41,
+   "id": "105b9478-5da4-4d3b-b7ac-85558c19b5c0",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "AsimovMapDataset\n",
-      "----------------\n",
-      "\n",
-      "  Name                            : asimov \n",
-      "\n",
-      "  Total counts                    : 2272781 \n",
-      "  Total background counts         : 2272694.54\n",
-      "  Total excess counts             : 87.14\n",
-      "\n",
-      "  Predicted counts                : 2272781.69\n",
-      "  Predicted background counts     : 2272694.54\n",
-      "  Predicted excess counts         : 87.14\n",
-      "\n",
-      "  Exposure min                    : 7.04e+07 m2 s\n",
-      "  Exposure max                    : 9.70e+11 m2 s\n",
-      "\n",
-      "  Number of total bins            : 90000 \n",
-      "  Number of fit bins              : 90000 \n",
-      "\n",
-      "  Fit statistic type              : cash\n",
-      "  Fit statistic value (-2 log(L)) : -14023900.06\n",
-      "\n",
-      "  Number of models                : 2 \n",
-      "  Number of parameters            : 4\n",
-      "  Number of free parameters       : 2\n",
-      "\n",
-      "  Component 0: SkyModel\n",
-      "  \n",
-      "    Name                      : asimov-darkmatter\n",
-      "    Datasets names            : None\n",
-      "    Spectral model type       : DarkMatterAnnihilationSpectralModel\n",
-      "    Spatial  model type       : TemplateSpatialModel\n",
-      "    Temporal model type       : \n",
-      "    Parameters:\n",
-      "      scale                         :      1.000   +/-    0.00             \n",
-      "  \n",
-      "  Component 1: FoVBackgroundModel\n",
-      "  \n",
-      "    Name                      : asimov-bkg\n",
-      "    Datasets names            : ['asimov']\n",
-      "    Spectral model type       : PowerLawNormSpectralModel\n",
-      "    Parameters:\n",
-      "      norm                          :      1.000   +/-    0.00             \n",
-      "      tilt                  (frozen):      0.000                   \n",
-      "      reference             (frozen):      1.000       TeV         \n",
-      "  \n",
-      "  \n"
+      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'pvalue_diff': 0.9305858800365727,\n",
+       " 'pvalue_same': 0.9996243278125864,\n",
+       " 'valid': True}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(asi_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da895fbe",
-   "metadata": {},
-   "source": [
-    "# Validation"
+    "validator.validate(n_toys=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "0385f66c-26aa-4d04-b5c8-0305903c6e22",
+   "execution_count": 42,
+   "id": "9619d2b5-34df-4bd8-b83c-fd9c52c873c9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from titrate.validation import AsymptoticValidator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e82fd440",
-   "metadata": {},
-   "source": [
-    "## QMuTestStatistic"
+    "validator.save_toys('/Users/stefan/Downloads/results.h5', overwrite=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "a0d66aaf-be8a-4f9f-81b8-77461b7cc43c",
+   "execution_count": 43,
+   "id": "8385fb4b-9573-41b4-a073-4daa9da7aec6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "validator = AsymptoticValidator(meas_dataset, asi_dataset, 'qmu', 'scale')"
+    "validator_h5 = AsymptoticValidator(meas_dataset, path='/Users/stefan/Downloads/results.h5', channel='b', mass=50*u.TeV)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "105b9478-5da4-4d3b-b7ac-85558c19b5c0",
+   "execution_count": 44,
+   "id": "ed93c895-952d-47f0-9272-78e443715d3b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'pvalue_diff': 0.8306038532710767,\n",
-       " 'pvalue_same': 0.864685260600026,\n",
+       "{'pvalue_diff': 0.9354541636482155,\n",
+       " 'pvalue_same': 0.9996243278125864,\n",
        " 'valid': True}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "validator.validate(n_toys=1000)"
+    "validator_h5.validate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "38727f0f-3297-4172-a819-9675b1c16a8a",
+   "execution_count": 45,
+   "id": "01b77368-82c6-4757-9504-5c5994a4a854",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/net/nfshome/home/sfroese/PHD/TITRATE/titrate/statistics.py:105: RuntimeWarning: divide by zero encountered in divide\n",
+      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:114: RuntimeWarning: divide by zero encountered in divide\n",
+      "  1\n",
+      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:103: RuntimeWarning: divide by zero encountered in divide\n",
       "  1\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -498,7 +422,11 @@
     }
    ],
    "source": [
-    "validator.plot_validation(n_toys=1000)"
+    "from titrate.plotting import ValidationPlotter\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "ValidationPlotter(meas_dataset, '/Users/stefan/Downloads/results.h5', statistic='qmu', channel='b', mass=50*u.TeV)\n",
+    "plt.savefig('/Users/stefan/Downloads/qmu_validation.pdf')"
    ]
   },
   {
@@ -511,62 +439,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 46,
    "id": "bf8e13c8-6bc5-49e4-9bde-fac5d203a552",
    "metadata": {},
    "outputs": [],
    "source": [
-    "validator_tilde = AsymptoticValidator(meas_dataset, asi_dataset, 'qtildemu', 'scale')"
+    "validator_tilde = AsymptoticValidator(meas_dataset, statistic='qtildemu', poi_name='scale')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 47,
    "id": "9e7aeaca-eabc-443b-bd65-1ac533258a35",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/scratch/sfroese/envs/titrate-dev/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "{'pvalue_diff': 0.9413103789420818,\n",
-       " 'pvalue_same': 0.3429201168560012,\n",
+       "{'pvalue_diff': 0.5837652762378469,\n",
+       " 'pvalue_same': 0.4205199265036619,\n",
        " 'valid': True}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "validator_tilde.validate(n_toys=1000)"
+    "validator_tilde.validate(n_toys=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 48,
    "id": "46104596-f6a4-47dc-b309-38573d723590",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "validator_tilde.save_toys('/Users/stefan/Downloads/results.h5', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "44fd21c7-0ce6-467c-9e70-90311d494722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validator_tilde_h5 = AsymptoticValidator(meas_dataset, path='/Users/stefan/Downloads/results.h5', statistic='qtildemu',channel='b', mass=50*u.TeV)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "87184481-cea3-4e4a-8a7c-43149f584e0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'pvalue_diff': 0.5837652762378469,\n",
+       " 'pvalue_same': 0.4205199265036619,\n",
+       " 'valid': True}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "validator_tilde_h5.validate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3bef315f-092a-419c-9f38-9ae0eba33482",
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/net/nfshome/home/sfroese/PHD/TITRATE/titrate/statistics.py:207: RuntimeWarning: divide by zero encountered in divide\n",
-      "  1\n"
+      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:247: RuntimeWarning: divide by zero encountered in divide\n",
+      "  1\n",
+      "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:234: RuntimeWarning: divide by zero encountered in divide\n",
+      "  1 / (2 * np.sqrt(2 * np.pi * ts_val)) * np.exp(-0.5 * ts_val),\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -576,7 +541,8 @@
     }
    ],
    "source": [
-    "validator_tilde.plot_validation(n_toys=1000)"
+    "ValidationPlotter(meas_dataset, '/Users/stefan/Downloads/results.h5', statistic='qtildemu', channel='b', mass=50*u.TeV)\n",
+    "plt.savefig('/Users/stefan/Downloads/qtildemu_validation.pdf')"
    ]
   },
   {
@@ -589,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 52,
    "id": "b248ff6b-fbe3-4c27-8b1b-564c4c806d22",
    "metadata": {},
    "outputs": [
@@ -621,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 349,
+   "execution_count": 53,
    "id": "16915873-726e-4272-b332-8ba9b690db60",
    "metadata": {},
    "outputs": [],
@@ -631,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 350,
+   "execution_count": 54,
    "id": "bff033b3-fb14-41a3-ae52-9e6203770ea6",
    "metadata": {},
    "outputs": [],
@@ -641,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 351,
+   "execution_count": 55,
    "id": "dcd6cdbd-e251-4726-8f11-5b795a46f4f6",
    "metadata": {
     "scrolled": true
@@ -678,9 +644,7 @@
       "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:125: RuntimeWarning: invalid value encountered in sqrt\n",
       "  return norm.cdf(np.sqrt(ts_val))\n",
       "/Users/stefan/Documents/projects/TITRATE/titrate/statistics.py:125: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  return norm.cdf(np.sqrt(ts_val))\n",
-      "/Users/stefan/mambaforge/envs/titrate-dev/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
-      "  warnings.warn(\n"
+      "  return norm.cdf(np.sqrt(ts_val))\n"
      ]
     }
    ],
@@ -690,179 +654,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 354,
+   "execution_count": 56,
    "id": "b38ea075-4e44-40c7-a755-7358d75948d4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mass': <Quantity [  0.1       ,   0.14384499,   0.20691381,   0.29763514,\n",
-      "             0.42813324,   0.61584821,   0.88586679,   1.27427499,\n",
-      "             1.83298071,   2.6366509 ,   3.79269019,   5.45559478,\n",
-      "             7.8475997 ,  11.28837892,  16.23776739,  23.35721469,\n",
-      "            33.59818286,  48.32930239,  69.51927962, 100.        ] TeV>, 'channel': array(['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b',\n",
-      "       'b', 'b', 'b', 'b', 'b', 'b', 'b'], dtype='<U1'), 'cl_type': array(['s', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's',\n",
-      "       's', 's', 's', 's', 's', 's', 's'], dtype='<U1'), 'cl': array([0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,\n",
-      "       0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95]), 'ul': <Quantity [1.27113561e-23, 1.80010407e-24, 8.03792494e-25, 3.55562386e-25,\n",
-      "           1.74311879e-25, 1.49304861e-25, 1.05396702e-25, 9.21410249e-26,\n",
-      "           1.06170263e-25, 9.78034772e-26, 7.77627941e-26, 1.02746942e-25,\n",
-      "           8.43862434e-26, 8.03963881e-26, 1.09371855e-25, 1.33494370e-25,\n",
-      "           1.31680870e-25, 1.14044209e-25, 2.35803100e-25, 1.82175739e-25] cm3 / s>, 'median_ul': <Quantity [8.32303298e-24, 1.15394639e-24, 4.47792372e-25, 1.80226953e-25,\n",
-      "           8.67029300e-26, 7.21693335e-26, 4.88121169e-26, 4.18750146e-26,\n",
-      "           4.87405231e-26, 4.74088963e-26, 4.26198836e-26, 6.07065485e-26,\n",
-      "           5.91957255e-26, 6.51735704e-26, 9.57059659e-26, 1.27899010e-25,\n",
-      "           1.45867065e-25, 1.50322941e-25, 3.11038147e-25, 2.81715109e-25] cm3 / s>, '1sigma_minus_ul': <Quantity [5.07920394e-24, 7.04205913e-25, 2.73269226e-25, 1.09985080e-25,\n",
-      "           5.29112242e-26, 4.40419694e-26, 2.97880229e-26, 2.55545953e-26,\n",
-      "           2.97443321e-26, 2.89316951e-26, 2.60091581e-26, 3.70467042e-26,\n",
-      "           3.61247112e-26, 3.97727435e-26, 5.84054059e-26, 7.80514938e-26,\n",
-      "           8.90166573e-26, 9.17358943e-26, 1.89813760e-25, 1.71919118e-25] cm3 / s>, '1sigma_plus_ul': <Quantity [1.31801006e-23, 1.82735422e-24, 7.09110310e-25, 2.85401892e-25,\n",
-      "           1.37300109e-25, 1.14285150e-25, 7.72973760e-26, 6.63119928e-26,\n",
-      "           7.71840022e-26, 7.50752786e-26, 6.74915446e-26, 9.61330342e-26,\n",
-      "           9.37405411e-26, 1.03206873e-25, 1.51557042e-25, 2.02536962e-25,\n",
-      "           2.30990625e-25, 2.38046813e-25, 4.92550500e-25, 4.46115433e-25] cm3 / s>, '2sigma_minus_ul': <Quantity [3.16827989e-24, 4.39265968e-25, 1.70458482e-25, 6.86059315e-26,\n",
-      "           3.30046932e-26, 2.74722747e-26, 1.85810208e-26, 1.59403149e-26,\n",
-      "           1.85537676e-26, 1.80468650e-26, 1.62238598e-26, 2.31088039e-26,\n",
-      "           2.25336878e-26, 2.48092388e-26, 3.64318258e-26, 4.86865621e-26,\n",
-      "           5.55263558e-26, 5.72225476e-26, 1.18401058e-25, 1.07238830e-25] cm3 / s>, '2sigma_plus_ul': <Quantity [1.93700731e-23, 2.68556258e-24, 1.04214065e-24, 4.19439555e-25,\n",
-      "           2.01782463e-25, 1.67958636e-25, 1.13599727e-25, 9.74551100e-26,\n",
-      "           1.13433108e-25, 1.10334032e-25, 9.91886328e-26, 1.41281464e-25,\n",
-      "           1.37765348e-25, 1.51677499e-25, 2.22735097e-25, 2.97657499e-25,\n",
-      "           3.39474292e-25, 3.49844385e-25, 7.23874538e-25, 6.55631459e-25] cm3 / s>}\n",
-      "{'mass': <Quantity [  0.1       ,   0.14384499,   0.20691381,   0.29763514,\n",
-      "             0.42813324,   0.61584821,   0.88586679,   1.27427499,\n",
-      "             1.83298071,   2.6366509 ,   3.79269019,   5.45559478,\n",
-      "             7.8475997 ,  11.28837892,  16.23776739,  23.35721469,\n",
-      "            33.59818286,  48.32930239,  69.51927962, 100.        ] TeV>, 'channel': array(['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W',\n",
-      "       'W', 'W', 'W', 'W', 'W', 'W', 'W'], dtype='<U1'), 'cl_type': array(['s', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's',\n",
-      "       's', 's', 's', 's', 's', 's', 's'], dtype='<U1'), 'cl': array([0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,\n",
-      "       0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95]), 'ul': <Quantity [2.69070716e-23, 1.32271337e-24, 2.43154713e-25, 1.23031130e-25,\n",
-      "           8.07100606e-26, 7.50064239e-26, 5.39685781e-26, 5.38035442e-26,\n",
-      "           6.67361629e-26, 5.73271904e-26, 3.72617201e-26, 5.61982523e-26,\n",
-      "           4.26208919e-26, 5.11362574e-26, 8.32358098e-26, 1.11614962e-25,\n",
-      "           1.24002790e-25, 1.24079569e-25, 2.56379835e-25, 2.27163632e-25] cm3 / s>, 'median_ul': <Quantity [1.76234288e-23, 8.56236822e-25, 1.45588555e-25, 5.76586853e-26,\n",
-      "           3.55702037e-26, 3.52323796e-26, 2.87294369e-26, 2.81967292e-26,\n",
-      "           3.51320949e-26, 3.48662499e-26, 3.09439336e-26, 4.51915204e-26,\n",
-      "           4.46887983e-26, 5.77289766e-26, 9.28656912e-26, 1.31816432e-25,\n",
-      "           1.61734063e-25, 1.80957200e-25, 3.74424254e-25, 3.68362690e-25] cm3 / s>, '1sigma_minus_ul': <Quantity [1.07548521e-23, 5.22526038e-25, 8.88466938e-26, 3.51867189e-26,\n",
-      "           2.17070291e-26, 2.15008690e-26, 1.75323911e-26, 1.72073016e-26,\n",
-      "           2.14396693e-26, 2.12774351e-26, 1.88838072e-26, 2.75785221e-26,\n",
-      "           2.72717315e-26, 3.52296148e-26, 5.66721033e-26, 8.04421351e-26,\n",
-      "           9.86996320e-26, 1.10430720e-25, 2.28495688e-25, 2.24796565e-25] cm3 / s>, '1sigma_plus_ul': <Quantity [2.79079230e-23, 1.35591045e-24, 2.30549584e-25, 9.13065312e-26,\n",
-      "           5.63278871e-26, 5.57929191e-26, 4.54950579e-26, 4.46514783e-26,\n",
-      "           5.56341113e-26, 5.52131273e-26, 4.90018673e-26, 7.15639100e-26,\n",
-      "           7.07678146e-26, 9.14178423e-26, 1.47059269e-25, 2.08740472e-25,\n",
-      "           2.56117118e-25, 2.86558289e-25, 5.92926801e-25, 5.83327893e-25] cm3 / s>, '2sigma_minus_ul': <Quantity [6.70860674e-24, 3.25938623e-25, 5.54203369e-26, 2.19485918e-26,\n",
-      "           1.35402997e-26, 1.34117022e-26, 1.09362654e-26, 1.07334827e-26,\n",
-      "           1.33735274e-26, 1.32723298e-26, 1.17792448e-26, 1.72027896e-26,\n",
-      "           1.70114214e-26, 2.19753492e-26, 3.53506352e-26, 5.01777842e-26,\n",
-      "           6.15663523e-26, 6.88839104e-26, 1.42529873e-25, 1.40222453e-25] cm3 / s>, '2sigma_plus_ul': <Quantity [4.10147485e-23, 1.99270745e-24, 3.38826118e-25, 1.34188216e-25,\n",
-      "           8.27820157e-26, 8.19958026e-26, 6.68615990e-26, 6.56218363e-26,\n",
-      "           8.17624114e-26, 8.11437143e-26, 7.20153650e-26, 1.05173566e-25,\n",
-      "           1.04003588e-25, 1.34351804e-25, 2.16124967e-25, 3.06774458e-25,\n",
-      "           3.76401324e-25, 4.21139049e-25, 8.71392100e-25, 8.57285109e-25] cm3 / s>}\n",
-      "{'mass': <Quantity [  0.1       ,   0.14384499,   0.20691381,   0.29763514,\n",
-      "             0.42813324,   0.61584821,   0.88586679,   1.27427499,\n",
-      "             1.83298071,   2.6366509 ,   3.79269019,   5.45559478,\n",
-      "             7.8475997 ,  11.28837892,  16.23776739,  23.35721469,\n",
-      "            33.59818286,  48.32930239,  69.51927962, 100.        ] TeV>, 'channel': array(['tau', 'tau', 'tau', 'tau', 'tau', 'tau', 'tau', 'tau', 'tau',\n",
-      "       'tau', 'tau', 'tau', 'tau', 'tau', 'tau', 'tau', 'tau', 'tau',\n",
-      "       'tau', 'tau'], dtype='<U3'), 'cl_type': array(['s', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's',\n",
-      "       's', 's', 's', 's', 's', 's', 's'], dtype='<U1'), 'cl': array([0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,\n",
-      "       0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95]), 'ul': <Quantity [4.10783906e-25, 5.59067758e-26, 2.69652246e-26, 2.04790107e-26,\n",
-      "           1.81364234e-26, 2.00124551e-26, 1.58625641e-26, 1.36649481e-26,\n",
-      "           1.62673874e-26, 1.69197974e-26, 1.37489149e-26, 1.86620418e-26,\n",
-      "           1.68068997e-26, 1.92102503e-26, 3.02834179e-26, 4.66604809e-26,\n",
-      "           7.32583564e-26, 1.30977273e-25, 2.70881661e-25, 5.78184643e-25] cm3 / s>, 'median_ul': <Quantity [2.68949064e-25, 3.59100334e-26, 1.50508899e-26, 9.28116748e-27,\n",
-      "           7.74513102e-27, 9.22595826e-27, 9.34586450e-27, 1.06032538e-26,\n",
-      "           1.44824460e-26, 1.64345331e-26, 1.59648795e-26, 2.33612277e-26,\n",
-      "           2.49271513e-26, 3.19757843e-26, 5.27000969e-26, 8.45627756e-26,\n",
-      "           1.35677787e-25, 2.43566789e-25, 5.03972959e-25, 1.07607625e-24] cm3 / s>, '1sigma_minus_ul': <Quantity [1.64128528e-25, 2.19144131e-26, 9.18493769e-27, 5.66391394e-27,\n",
-      "           4.72653420e-27, 5.63022202e-27, 5.70339586e-27, 6.47072872e-27,\n",
-      "           8.83803980e-27, 1.00293181e-26, 9.74270785e-27, 1.42563943e-26,\n",
-      "           1.52120129e-26, 1.95135031e-26, 3.21606968e-26, 5.16051762e-26,\n",
-      "           8.27985603e-26, 1.48638771e-25, 3.07553922e-25, 6.56684981e-25] cm3 / s>, '1sigma_plus_ul': <Quantity [4.25899516e-25, 5.68660310e-26, 2.38341291e-26, 1.46973731e-26,\n",
-      "           1.22649527e-26, 1.46099454e-26, 1.47998253e-26, 1.67909885e-26,\n",
-      "           2.29339587e-26, 2.60252242e-26, 2.52814950e-26, 3.69941259e-26,\n",
-      "           3.94738746e-26, 5.06358744e-26, 8.34542623e-26, 1.33911026e-25,\n",
-      "           2.14855195e-25, 3.85704920e-25, 7.98076169e-25, 1.70404146e-24] cm3 / s>, '2sigma_minus_ul': <Quantity [1.02379255e-25, 1.36696607e-26, 5.72933352e-27, 3.53300731e-27,\n",
-      "           2.94829336e-27, 3.51199114e-27, 3.55763514e-27, 4.03627811e-27,\n",
-      "           5.51294733e-27, 6.25603684e-27, 6.07725654e-27, 8.89278082e-27,\n",
-      "           9.48887171e-27, 1.21720333e-26, 2.00610352e-26, 3.21900132e-26,\n",
-      "           5.16476631e-26, 9.27171336e-26, 1.91844415e-25, 4.09623604e-25] cm3 / s>, '2sigma_plus_ul': <Quantity [6.25921230e-25, 8.35728964e-26, 3.50277163e-26, 2.15999256e-26,\n",
-      "           1.80251303e-26, 2.14714379e-26, 2.17504940e-26, 2.46767977e-26,\n",
-      "           3.37047851e-26, 3.82478490e-26, 3.71548309e-26, 5.43682441e-26,\n",
-      "           5.80125951e-26, 7.44167759e-26, 1.22648166e-25, 1.96801713e-25,\n",
-      "           3.15760931e-25, 5.66849432e-25, 1.17288891e-24, 2.50433656e-24] cm3 / s>}\n",
-      "{'mass': <Quantity [  0.1       ,   0.14384499,   0.20691381,   0.29763514,\n",
-      "             0.42813324,   0.61584821,   0.88586679,   1.27427499,\n",
-      "             1.83298071,   2.6366509 ,   3.79269019,   5.45559478,\n",
-      "             7.8475997 ,  11.28837892,  16.23776739,  23.35721469,\n",
-      "            33.59818286,  48.32930239,  69.51927962, 100.        ] TeV>, 'channel': array(['mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu',\n",
-      "       'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu', 'mu'], dtype='<U2'), 'cl_type': array(['s', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's', 's',\n",
-      "       's', 's', 's', 's', 's', 's', 's'], dtype='<U1'), 'cl': array([0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,\n",
-      "       0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95]), 'ul': <Quantity [4.88126894e-25, 8.40601534e-26, 8.15250150e-26, 7.61098461e-26,\n",
-      "           5.88742815e-26, 5.43158870e-26, 3.88356258e-26, 4.64920099e-26,\n",
-      "           6.89703084e-26, 6.60049650e-26, 4.06851859e-26, 6.97741582e-26,\n",
-      "           7.03858521e-26, 8.61582682e-26, 1.58911612e-25, 2.88772041e-25,\n",
-      "           5.10839812e-25, 8.85168938e-25, 1.83362498e-24, 2.82186605e-24] cm3 / s>, 'median_ul': <Quantity [3.19450504e-25, 5.35053785e-26, 3.99026729e-26, 3.11838962e-26,\n",
-      "           2.97947735e-26, 3.82786504e-26, 3.96733646e-26, 4.53518822e-26,\n",
-      "           6.05416966e-26, 6.42811591e-26, 6.10305754e-26, 9.58491250e-26,\n",
-      "           1.13183165e-25, 1.55396543e-25, 2.86402525e-25, 5.21450857e-25,\n",
-      "           9.20210883e-25, 1.59061227e-24, 3.29119788e-24, 5.04108961e-24] cm3 / s>, '1sigma_minus_ul': <Quantity [1.94947474e-25, 3.26521269e-26, 2.43509564e-26, 1.90302465e-26,\n",
-      "           1.81825221e-26, 2.33598825e-26, 2.42110190e-26, 2.76763843e-26,\n",
-      "           3.69461019e-26, 3.92281416e-26, 3.72444445e-26, 5.84927700e-26,\n",
-      "           6.90710198e-26, 9.48321046e-26, 1.74779655e-25, 3.18219963e-25,\n",
-      "           5.61566770e-25, 9.70685100e-25, 2.00848240e-24, 3.07636919e-24] cm3 / s>, '1sigma_plus_ul': <Quantity [5.05872051e-25, 8.47294815e-26, 6.31886528e-26, 4.93818647e-26,\n",
-      "           4.71820924e-26, 6.06169004e-26, 6.28255272e-26, 7.18178541e-26,\n",
-      "           9.58719798e-26, 1.01793678e-25, 9.66461533e-26, 1.51783744e-25,\n",
-      "           1.79233399e-25, 2.46081215e-25, 4.53538282e-25, 8.25753634e-25,\n",
-      "           1.45721782e-24, 2.51884495e-24, 5.21184035e-24, 7.98291538e-24] cm3 / s>, '2sigma_minus_ul': <Quantity [1.21603340e-25, 2.03675770e-26, 1.51895152e-26, 1.18705899e-26,\n",
-      "           1.13418008e-26, 1.45713082e-26, 1.51022258e-26, 1.72638336e-26,\n",
-      "           2.30460507e-26, 2.44695298e-26, 2.32321493e-26, 3.64863213e-26,\n",
-      "           4.30847679e-26, 5.91538859e-26, 1.09023161e-25, 1.98497623e-25,\n",
-      "           3.50291252e-25, 6.05488994e-25, 1.25284089e-24, 1.91896186e-24] cm3 / s>, '2sigma_plus_ul': <Quantity [7.43452493e-25, 1.24522286e-25, 9.28649079e-26, 7.25738264e-26,\n",
-      "           6.93409413e-26, 8.90853441e-26, 9.23312421e-26, 1.05546773e-25,\n",
-      "           1.40897807e-25, 1.49600603e-25, 1.42035567e-25, 2.23068269e-25,\n",
-      "           2.63409526e-25, 3.61652106e-25, 6.66540414e-25, 1.21356496e-24,\n",
-      "           2.14159335e-24, 3.70180870e-24, 7.65955678e-24, 1.17320542e-23] cm3 / s>}\n"
-     ]
-    }
-   ],
-   "source": [
-    "ulfactory.save_results('/Users/stefan/Downloads/test2.hdf5', overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 340,
-   "id": "ecd17295-d3de-472e-9c58-84888a3805a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['W', 'b', 'mu', 'tau']"
-      ]
-     },
-     "execution_count": 340,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from astropy.table import QTable\n",
-    "import h5py\n",
-    "\n",
-    "channels = list(h5py.File('/Users/stefan/Downloads/test.hdf5').keys())\n",
-    "a = [channel for channel in channels if 'meta' not in channel]\n",
-    "a"
+    "ulfactory.save_results('/Users/stefan/Downloads/results.h5', overwrite=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 355,
+   "execution_count": 57,
    "id": "35df44ae-8b80-4603-9efd-91b4e45aee13",
    "metadata": {},
    "outputs": [],
@@ -872,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 357,
+   "execution_count": 58,
    "id": "13872526-2277-4505-9090-18c462953d2e",
    "metadata": {},
    "outputs": [
@@ -891,8 +693,8 @@
     "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10,8),layout='constrained')\n",
     "\n",
     "for channel, ax in zip(['b', 'W', 'tau', 'mu'], np.array(axs).reshape(-1)):\n",
-    "    UpperLimitPlotter('/Users/stefan/Downloads/test2.hdf5', channel=channel, axes=ax)\n",
-    "fig.savefig('/Users/stefan/Downloads/compare420.pdf')"
+    "    UpperLimitPlotter('/Users/stefan/Downloads/results.h5', channel=channel, ax=ax)\n",
+    "fig.savefig('/Users/stefan/Downloads/upperlimits.pdf')"
    ]
   },
   {
@@ -902,6 +704,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96ea6ea3-8db3-40e6-8d33-9c09a9640d4c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/setup.cfg b/setup.cfg
index d68c8bc..db2040d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = titrate
-version = 0.4.0
+version = 0.4.1
 author = Stefan Fröse
 author_email = stefan.froese@tu-dortmund.de
 description = asympTotic lIkelihood Tests for daRk mAtTer sEarch
diff --git a/titrate/plotting.py b/titrate/plotting.py
index 7d9ff50..2448708 100644
--- a/titrate/plotting.py
+++ b/titrate/plotting.py
@@ -1,18 +1,25 @@
 import h5py
 import matplotlib.pyplot as plt
+import numpy as np
 from astropy import visualization as viz
 from astropy.table import QTable, unique
+from astropy.units import Quantity
+
+from titrate.datasets import AsimovMapDataset
+from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic
+
+STATISTICS = {"qmu": QMuTestStatistic, "qtildemu": QTildeMuTestStatistic}
 
 
 class UpperLimitPlotter:
-    def __init__(self, path, channel, axes=None):
+    def __init__(self, path, channel, ax=None):
         self.path = path
-        self.axes = axes if axes is not None else plt.gca()
+        self.ax = ax if ax is not None else plt.gca()
 
         try:
-            table = QTable.read(self.path, path=channel)
+            table = QTable.read(self.path, path=f"upperlimits/{channel}")
         except OSError:
-            channels = list(h5py.File("/Users/stefan/Downloads/test.hdf5").keys())
+            channels = list(h5py.File(self.path).keys())
             channels = [ch for ch in channels if "meta" not in ch]
             raise KeyError(
                 f"Channel {channel} not in dataframe. " f"Choose from {channels}"
@@ -39,21 +46,21 @@ def __init__(self, path, channel, axes=None):
                 two_sigma_plus,
             )
 
-        self.axes.set_xscale("log")
-        self.axes.set_yscale("log")
+        self.ax.set_xscale("log")
+        self.ax.set_yscale("log")
 
         cl_type = unique(table[table["channel"] == self.channel], keys="cl_type")[
             "cl_type"
         ][0]
         cl = unique(table[table["channel"] == self.channel], keys="cl")["cl"][0]
-        self.axes.set_xlabel(f"m / {masses.unit:latex}")
-        self.axes.set_ylabel(
+        self.ax.set_xlabel(f"m / {masses.unit:latex}")
+        self.ax.set_ylabel(
             rf"$CL_{cl_type}^{{{cl}}}$ upper limit on $< \sigma v>$ / {uls.unit:latex}"
         )
 
-        self.axes.set_title(f"Annihilation Upper Limits for channel {self.channel}")
+        self.ax.set_title(f"Annihilation Upper Limits for channel {self.channel}")
 
-        self.axes.legend()
+        self.ax.legend()
 
     def plot_channel(
         self,
@@ -65,9 +72,9 @@ def plot_channel(
         two_sigma_minus,
         two_sigma_plus,
     ):
-        self.axes.plot(masses, uls, color="tab:orange", label="Upper Limits")
-        self.axes.plot(masses, median, color="tab:blue", label="Expected Upper Limits")
-        self.axes.fill_between(
+        self.ax.plot(masses, uls, color="tab:orange", label="Upper Limits")
+        self.ax.plot(masses, median, color="tab:blue", label="Expected Upper Limits")
+        self.ax.fill_between(
             masses,
             median,
             one_sigma_plus,
@@ -75,10 +82,10 @@ def plot_channel(
             alpha=0.75,
             label=r"$1\sigma$-region",
         )
-        self.axes.fill_between(
+        self.ax.fill_between(
             masses, median, one_sigma_minus, color="tab:blue", alpha=0.75
         )
-        self.axes.fill_between(
+        self.ax.fill_between(
             masses,
             one_sigma_plus,
             two_sigma_plus,
@@ -86,6 +93,103 @@ def plot_channel(
             alpha=0.5,
             label=r"$2\sigma$-region",
         )
-        self.axes.fill_between(
+        self.ax.fill_between(
             masses, one_sigma_minus, two_sigma_minus, color="tab:blue", alpha=0.5
         )
+
+
+class ValidationPlotter:
+    def __init__(
+        self,
+        measurement_dataset,
+        path,
+        channel=None,
+        mass=None,
+        statistic="qmu",
+        poi_name="scale",
+        ax=None,
+    ):
+        self.path = path
+        self.ax = ax if ax is not None else plt.gca()
+
+        asimov_dataset = AsimovMapDataset.from_MapDataset(measurement_dataset)
+
+        try:
+            table = QTable.read(
+                self.path, path=f"validation/{statistic}/{channel}/{mass}"
+            )
+        except OSError:
+            if channel is None:
+                channels = list(h5py.File(self.path)["validation"][statistic].keys())
+                channels = [ch for ch in channels if "meta" not in ch]
+                raise ValueError(f"Channel must be one of {channels}")
+            if mass is None:
+                masses = list(
+                    h5py.File(self.path)["validation"][statistic][channel].keys()
+                )
+                masses = [Quantity(m) for m in masses if "meta" not in m]
+                raise ValueError(f"Mass must be one of {masses}")
+
+        toys_ts_same = table["toys_ts_same"]
+        toys_ts_diff = table["toys_ts_diff"]
+
+        max_ts = max(toys_ts_diff.max(), toys_ts_same.max())
+        bins = np.linspace(0, max_ts, 31)
+        linspace = np.linspace(0, max_ts, 1000)
+        statistic = STATISTICS[statistic](asimov_dataset, poi_name)
+        statistic_math_name = (
+            r"q_\mu" if isinstance(statistic, QMuTestStatistic) else r"\tilde{q}_\mu"
+        )
+
+        self.plot(
+            linspace, bins, toys_ts_same, toys_ts_diff, statistic, statistic_math_name
+        )
+
+        self.ax.set_yscale("log")
+        self.ax.set_xlim(0, max_ts)
+
+        self.ax.set_ylabel("pdf")
+        self.ax.set_xlabel(rf"${statistic_math_name}$")
+        self.ax.set_title(statistic.__class__.__name__)
+        self.ax.legend()
+
+    def plot(
+        self, linspace, bins, toys_ts_same, toys_ts_diff, statistic, statistic_math_name
+    ):
+        plt.hist(
+            toys_ts_diff,
+            bins=bins,
+            density=True,
+            histtype="step",
+            color="tab:blue",
+            label=(
+                rf"$f({statistic_math_name}\vert\mu^\prime)$, "
+                r"$\mu=1$, $\mu^\prime=0$"
+            ),
+        )
+        plt.hist(
+            toys_ts_same,
+            bins=bins,
+            density=True,
+            histtype="step",
+            color="tab:orange",
+            label=(
+                rf"$f({statistic_math_name}\vert\mu^\prime)$, "
+                r"$\mu=1$, $\mu^\prime=1$"
+            ),
+        )
+
+        plt.plot(
+            linspace,
+            statistic.asympotic_approximation_pdf(
+                poi_val=1, same=False, poi_true_val=0, ts_val=linspace
+            ),
+            color="tab:blue",
+            label=rf"$f({statistic_math_name}\vert\mu^\prime)$, asympotic",
+        )
+        plt.plot(
+            linspace,
+            statistic.asympotic_approximation_pdf(poi_val=1, ts_val=linspace),
+            color="tab:orange",
+            label=rf"$f({statistic_math_name}\vert\mu^\prime)$, asympotic",
+        )
diff --git a/titrate/tests/test_upperlimits.py b/titrate/tests/test_upperlimits.py
index 770d626..1a29910 100644
--- a/titrate/tests/test_upperlimits.py
+++ b/titrate/tests/test_upperlimits.py
@@ -43,7 +43,7 @@ def upperlimits_file(jfact_map, measurement_dataset, tmp_path_factory):
 
 @pytest.mark.parametrize("channel", ["b", "W"])
 def test_ULFactory(upperlimits_file, channel):
-    table = QTable.read(upperlimits_file, path=channel)
+    table = QTable.read(upperlimits_file, path=f"upperlimits/{channel}")
     assert np.all(table["mass"] == np.geomspace(0.1, 100, 5) * u.TeV)
     assert len(table["ul"]) == 5
     assert len(table["median_ul"]) == 5
@@ -64,4 +64,4 @@ def test_UpperLimitPlotter(upperlimits_file):
     fig, axs = plt.subplots(nrows=1, ncols=2)
 
     for channel, ax in zip(["b", "W"], np.array(axs).reshape(-1)):
-        UpperLimitPlotter(upperlimits_file, channel=channel, axes=ax)
+        UpperLimitPlotter(upperlimits_file, channel=channel, ax=ax)
diff --git a/titrate/tests/test_validation.py b/titrate/tests/test_validation.py
index 2776749..ce91b5a 100644
--- a/titrate/tests/test_validation.py
+++ b/titrate/tests/test_validation.py
@@ -1,11 +1,15 @@
+import astropy.units as u
 import numpy as np
 import pytest
 
 
-def test_AsmyptoticValidator(measurement_dataset, asimov_dataset):
+@pytest.fixture(scope="module")
+def validation_file(measurement_dataset, tmp_path_factory):
     from titrate.validation import AsymptoticValidator
 
-    validator = AsymptoticValidator(measurement_dataset, asimov_dataset, "qmu", "scale")
+    data = tmp_path_factory.mktemp("data")
+
+    validator = AsymptoticValidator(measurement_dataset, "qmu", "scale")
     result = validator.validate(n_toys=10)
     assert list(result.keys()) == ["pvalue_diff", "pvalue_same", "valid"]
     assert result["pvalue_diff"] != 0
@@ -14,19 +18,56 @@ def test_AsmyptoticValidator(measurement_dataset, asimov_dataset):
     assert result["pvalue_same"] != np.nan
     assert isinstance(result["valid"], np.bool_)
 
-    # same for qtildemu
-    validator_tilde = AsymptoticValidator(
-        measurement_dataset, asimov_dataset, "qtildemu", "scale"
-    )
+    with pytest.raises(ValueError) as excinfo:
+        AsymptoticValidator(measurement_dataset, "stupidTest", "scale")
+
+    assert str(excinfo.value) == "Statistic must be one of ['qmu', 'qtildemu']"
+
+    validator.save_toys(f"{data}/val.h5")
+
+    validator_tilde = AsymptoticValidator(measurement_dataset, "qtildemu", "scale")
     result_tilde = validator_tilde.validate(n_toys=10)
-    assert list(result_tilde.keys()) == ["pvalue_diff", "pvalue_same", "valid"]
+    assert list(result.keys()) == ["pvalue_diff", "pvalue_same", "valid"]
     assert result_tilde["pvalue_diff"] != 0
     assert result_tilde["pvalue_diff"] != np.nan
     assert result_tilde["pvalue_same"] != 0
     assert result_tilde["pvalue_same"] != np.nan
     assert isinstance(result_tilde["valid"], np.bool_)
 
-    with pytest.raises(ValueError) as excinfo:
-        AsymptoticValidator(measurement_dataset, asimov_dataset, "stupidTest", "scale")
+    validator_tilde.save_toys(f"{data}/val.h5")
 
-    assert str(excinfo.value) == "Statistic must be one of ['qmu', 'qtildemu']"
+    return f"{data}/val.h5"
+
+
+@pytest.mark.parametrize("statistic", ["qmu", "qtildemu"])
+def test_AsmyptoticValidator(measurement_dataset, statistic, validation_file):
+    from titrate.validation import AsymptoticValidator
+
+    validator = AsymptoticValidator(
+        measurement_dataset,
+        statistic=statistic,
+        path=validation_file,
+        channel="b",
+        mass=50 * u.TeV,
+    )
+    result = validator.validate()
+
+    assert list(result.keys()) == ["pvalue_diff", "pvalue_same", "valid"]
+    assert result["pvalue_diff"] != 0
+    assert result["pvalue_diff"] != np.nan
+    assert result["pvalue_same"] != 0
+    assert result["pvalue_same"] != np.nan
+    assert isinstance(result["valid"], np.bool_)
+
+
+@pytest.mark.parametrize("statistic", ["qmu", "qtildemu"])
+def test_ValidationPlotter(measurement_dataset, statistic, validation_file):
+    from titrate.plotting import ValidationPlotter
+
+    ValidationPlotter(
+        measurement_dataset,
+        path=validation_file,
+        statistic=statistic,
+        channel="b",
+        mass=50 * u.TeV,
+    )
diff --git a/titrate/upperlimits.py b/titrate/upperlimits.py
index a587ef0..eeb664c 100644
--- a/titrate/upperlimits.py
+++ b/titrate/upperlimits.py
@@ -242,7 +242,7 @@ def save_results(self, path, overwrite=False, **kwargs):
             qtable.write(
                 path,
                 format="hdf5",
-                path=f"{channel}",
+                path=f"upperlimits/{channel}",
                 overwrite=overwrite,
                 append=True,
                 serialize_meta=True,
diff --git a/titrate/validation.py b/titrate/validation.py
index 5ddeea1..9c37ddc 100644
--- a/titrate/validation.py
+++ b/titrate/validation.py
@@ -1,9 +1,14 @@
 from functools import lru_cache
 
-import matplotlib.pyplot as plt
+import h5py
 import numpy as np
+from astropy.table import QTable
+from astropy.units import Quantity
+from gammapy.astro.darkmatter import DarkMatterAnnihilationSpectralModel
+from gammapy.modeling.models import SkyModel
 from joblib import Parallel, delayed
 
+from titrate.datasets import AsimovMapDataset
 from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic, kstest
 from titrate.utils import calc_ts_toyMC
 
@@ -12,7 +17,13 @@
 
 class AsymptoticValidator:
     def __init__(
-        self, measurement_dataset, asimov_dataset, statistic="qmu", poi_name=""
+        self,
+        measurement_dataset,
+        statistic="qmu",
+        poi_name="scale",
+        path=None,
+        channel=None,
+        mass=None,
     ):
         if statistic not in STATISTICS.keys():
             raise ValueError(
@@ -20,28 +31,45 @@ def __init__(
             )
         self.statistic_key = statistic
         self.statistic = STATISTICS[statistic]
+
         self.measurement_dataset = measurement_dataset
-        self.asimov_dataset = asimov_dataset
+        self.asimov_dataset = AsimovMapDataset.from_MapDataset(self.measurement_dataset)
+
+        self.path = path
+        self.channel = channel
+        self.mass = mass
+        if self.channel is None and self.path is not None:
+            channels = list(
+                h5py.File(self.path)["validation"][self.statistic_key].keys()
+            )
+            channels = [ch for ch in channels if "meta" not in ch]
+            raise ValueError(f"Channel must be one of {channels}")
+        if self.mass is None and self.path is not None:
+            masses = list(
+                h5py.File(self.path)["validation"][self.statistic_key][
+                    self.channel
+                ].keys()
+            )
+            masses = [Quantity(m) for m in masses if "meta" not in m]
+            raise ValueError(f"Mass must be one of {masses}")
+
         self.poi_name = poi_name
 
-    def validate(self, n_toys=1000):
-        toys_ts_diff = self.toys_ts(n_toys, 1, 0)
-        toys_ts_same = self.toys_ts(n_toys, 1, 1)
+        self.toys_ts_diff = None
+        self.toys_ts_same = None
 
-        # only validate ts values above zero because
-        # QTildeMuTestStatistic cdf will have problems with negative values in sqrt
-        toys_ts_diff = toys_ts_diff[toys_ts_diff >= 0]
-        toys_ts_same = toys_ts_same[toys_ts_same >= 0]
+    def validate(self, n_toys=1000):
+        self.generate_datasets(n_toys)
 
         stat = self.statistic(self.asimov_dataset, self.poi_name)
         ks_diff = kstest(
-            toys_ts_diff,
+            self.toys_ts_diff[self.toys_ts_diff >= 0],
             lambda x: stat.asympotic_approximation_cdf(
                 poi_val=1, same=False, poi_true_val=0, ts_val=x
             ),
         )
         ks_same = kstest(
-            toys_ts_same,
+            self.toys_ts_same[self.toys_ts_same >= 0],
             lambda x: stat.asympotic_approximation_cdf(poi_val=1, ts_val=x),
         )
 
@@ -49,6 +77,16 @@ def validate(self, n_toys=1000):
 
         return {"pvalue_diff": ks_diff, "pvalue_same": ks_same, "valid": valid}
 
+    def generate_datasets(self, n_toys):
+        if self.path is None:
+            toys_ts_diff = self.toys_ts(n_toys, 1, 0)
+            toys_ts_same = self.toys_ts(n_toys, 1, 1)
+        else:
+            toys_ts_same, toys_ts_diff = self.open_toys()
+
+        self.toys_ts_diff = toys_ts_diff
+        self.toys_ts_same = toys_ts_same
+
     @lru_cache
     def toys_ts(self, n_toys, poi_val, poi_true_val):
         toys_ts = Parallel(n_jobs=-1, verbose=0)(
@@ -67,52 +105,51 @@ def toys_ts(self, n_toys, poi_val, poi_true_val):
 
         return toys_ts
 
-    def plot_validation(self, n_toys=1000):
-        toys_ts_diff = self.toys_ts(n_toys, 1, 0)
-        toys_ts_same = self.toys_ts(n_toys, 1, 1)
-
-        max_q = max(toys_ts_diff.max(), toys_ts_same.max())
-        bins = np.linspace(0, max_q, 31)
-        plt.hist(
-            toys_ts_diff,
-            bins=bins,
-            density=True,
-            histtype="step",
-            color="tab:blue",
-            label=r"$f(q_\mu\vert\mu^\prime)$, poi_val=1, poi_true_val=0",
-        )
-        plt.hist(
-            toys_ts_same,
-            bins=bins,
-            density=True,
-            histtype="step",
-            color="tab:orange",
-            label=r"$f(q_\mu\vert\mu)$, poi_val=1, poi_true_val=1",
+    def open_toys(self):
+        toys = QTable.read(
+            self.path,
+            path=f"validation/{self.statistic_key}/{self.channel}/{self.mass}",
         )
 
-        lin_q = np.linspace(0, max_q, 1000)
-        stat = self.statistic(self.asimov_dataset, self.poi_name)
+        toys_ts_diff = toys["toys_ts_diff"]
+        toys_ts_same = toys["toys_ts_same"]
+
+        return toys_ts_same, toys_ts_diff
+
+    def save_toys(self, path, overwrite=False, **kwargs):
+        if self.toys_ts_diff is None or self.toys_ts_same is None:
+            raise ValueError("Toys not generated yet. Run validate() first.")
+
+        # collect meta data
+        for model in self.measurement_dataset.models:
+            if isinstance(model, SkyModel):
+                if isinstance(
+                    model.spectral_model, DarkMatterAnnihilationSpectralModel
+                ):
+                    channel = model.spectral_model.channel
+                    mass = model.spectral_model.mass
+        try:
+            channel
+            mass
+        except NameError:
+            raise NameError(
+                "Could not find channel and mass in measurement dataset. "
+                "Please add a DarkMatterAnnihilationSpectralModel to the dataset."
+            )
 
-        plt.plot(
-            lin_q,
-            stat.asympotic_approximation_pdf(
-                poi_val=1, same=False, poi_true_val=0, ts_val=lin_q
-            ),
-            color="tab:blue",
-            label=r"$f(q_\mu\vert\mu^\prime)$, asympotic",
+        # save toys
+        toys_dict = {
+            "toys_ts_diff": self.toys_ts_diff,
+            "toys_ts_same": self.toys_ts_same,
+        }
+
+        qtable = QTable(toys_dict)
+        qtable.write(
+            path,
+            format="hdf5",
+            path=f"validation/{self.statistic_key}/{channel}/{mass}",
+            overwrite=overwrite,
+            append=True,
+            serialize_meta=True,
+            **kwargs,
         )
-        plt.plot(
-            lin_q,
-            stat.asympotic_approximation_pdf(poi_val=1, ts_val=lin_q),
-            color="tab:orange",
-            label=r"$f(q_\mu\vert\mu)$, asympotic",
-        )
-
-        plt.yscale("log")
-        plt.xlim(0, max_q)
-
-        plt.ylabel("pdf")
-        plt.xlabel("q")
-        plt.title(self.statistic.__name__)
-        plt.legend()
-        plt.show()