diff --git a/tutorials/Mean_Absolute_Error.ipynb b/tutorials/Mean_Absolute_Error.ipynb
index e784b062..52aa64a2 100644
--- a/tutorials/Mean_Absolute_Error.ipynb
+++ b/tutorials/Mean_Absolute_Error.ipynb
@@ -10,64 +10,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "id": "cf78b685-ec20-4935-848f-333fc7fc6735",
    "metadata": {
     "scrolled": true,
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function mae in module scores.continuous.standard_impl:\n",
-      "\n",
-      "mae(fcst: Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset, pandas.core.series.Series], obs: Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset, pandas.core.series.Series], reduce_dims: Optional[collections.abc.Iterable[collections.abc.Hashable]] = None, preserve_dims: Optional[collections.abc.Iterable[collections.abc.Hashable]] = None, weights: xarray.core.dataarray.DataArray = None, angular: bool = False) -> Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset, pandas.core.series.Series]\n",
-      "    Calculates the mean absolute error from forecast and observed data.\n",
-      "    \n",
-      "    A detailed explanation is on [Wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_error)\n",
-      "    \n",
-      "    Dimensional reduction is not supported for pandas and the user should\n",
-      "    convert their data to xarray to formulate the call to the metric.\n",
-      "    At most one of reduce_dims and preserve_dims may be specified.\n",
-      "    Specifying both will result in an exception.\n",
-      "    \n",
-      "    Args:\n",
-      "        fcst: Forecast or predicted variables in xarray or pandas.\n",
-      "        obs: Observed variables in xarray or pandas.\n",
-      "        reduce_dims: Optionally specify which dimensions to reduce when\n",
-      "            calculating MAE. All other dimensions will be preserved.\n",
-      "        preserve_dims: Optionally specify which dimensions to preserve when\n",
-      "            calculating MAE. All other dimensions will be reduced. As a\n",
-      "            special case, 'all' will allow all dimensions to be preserved. In\n",
-      "            this case, the result will be in the same shape/dimensionality\n",
-      "            as the forecast, and the errors will be the absolute error at each\n",
-      "            point (i.e. single-value comparison against observed), and the\n",
-      "            forecast and observed dimensions must match precisely.\n",
-      "        weights: Optionally provide an array for weighted averaging (e.g. by area, by latitude,\n",
-      "            by population, custom)\n",
-      "        angular: specifies whether `fcst` and `obs` are angular\n",
-      "            data (e.g. wind direction). If True, a different function is used\n",
-      "            to calculate the difference between `fcst` and `obs`, which\n",
-      "            accounts for circularity. Angular `fcst` and `obs` data should be in\n",
-      "            degrees rather than radians.\n",
-      "    \n",
-      "    Returns:\n",
-      "        By default an xarray DataArray containing\n",
-      "        a single floating point number representing the mean absolute error for the\n",
-      "        supplied data. All dimensions will be reduced.\n",
-      "    \n",
-      "        Alternatively, an xarray structure with dimensions preserved as appropriate\n",
-      "        containing the score along reduced dimensions\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from scores.continuous import mae\n",
     "\n",
-    "help(mae)"
+    "# Uncomment the line below to view detailed help information on the arguments to the MAE function\n",
+    "# help(mae)"
    ]
   },
   {
@@ -956,7 +910,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,