task: metadata: install: pip install -e .
patch: diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 37f98f6..1d222db 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -11,6 +11,11 @@ Features
   :py:func:`~xskillscore.me`. (:issue:`202`, :pr:`200`)
   `Andrew Huang`_
 
+Bug Fixes
+~~~~~~~~~
+- :py:func:`~xskillscore.sign_test` now works for ``xr.Dataset`` inputs.
+  (:issue:`198`, :pr:`199`) `Aaron Spring`_
+
 Internal Changes
 ~~~~~~~~~~~~~~~~
 - Added Python 3.7 and Python 3.8 to the CI. Use the latest version of Python 3
diff --git a/docs/source/quick-start.ipynb b/docs/source/quick-start.ipynb
index 1b12123..f53c938 100644
--- a/docs/source/quick-start.ipynb
+++ b/docs/source/quick-start.ipynb
@@ -604,7 +604,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/aaron.spring/Coding/xskillscore/xskillscore/core/probabilistic.py:642: RuntimeWarning: invalid value encountered in long_scalars\n",
+      "/Users/aaron.spring/Coding/xskillscore/xskillscore/core/probabilistic.py:670: RuntimeWarning: invalid value encountered in long_scalars\n",
       "  r[..., i] = N_o_f_in_bin / N_f_in_bin\n"
      ]
     }
@@ -1009,16 +1009,16 @@
        "    observations_category_bounds  (observations_category) &lt;U10 &#x27;[0.0, 0.5)&#x27; &#x27;...\n",
        "    forecasts_category_bounds     (forecasts_category) &lt;U10 &#x27;[0.0, 0.5)&#x27; &#x27;[0....\n",
        "  * observations_category         (observations_category) int64 1 2\n",
-       "  * forecasts_category            (forecasts_category) int64 1 2</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'histogram_observations_forecasts'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>observations_category</span>: 2</li><li><span class='xr-has-index'>forecasts_category</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-de7eba3c-2fb8-4028-abef-bcb6b615793c' class='xr-array-in' type='checkbox' checked><label for='section-de7eba3c-2fb8-4028-abef-bcb6b615793c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5 6 6 3 6 5 4 5 5 5 4 6</span></div><div class='xr-array-data'><pre>array([[[5, 6],\n",
+       "  * forecasts_category            (forecasts_category) int64 1 2</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'histogram_observations_forecasts'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>observations_category</span>: 2</li><li><span class='xr-has-index'>forecasts_category</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6a435e47-7e85-4782-a343-0ffcf30928c6' class='xr-array-in' type='checkbox' checked><label for='section-6a435e47-7e85-4782-a343-0ffcf30928c6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5 6 6 3 6 5 4 5 5 5 4 6</span></div><div class='xr-array-data'><pre>array([[[5, 6],\n",
        "        [6, 3]],\n",
        "\n",
        "       [[6, 5],\n",
        "        [4, 5]],\n",
        "\n",
        "       [[5, 5],\n",
-       "        [4, 6]]])</pre></div></div></li><li class='xr-section-item'><input id='section-bf632e65-c01d-4171-a184-64978dfba467' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf632e65-c01d-4171-a184-64978dfba467' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2000-01-01 00:00:00 ... 2000-01-...</div><input id='attrs-2c0e10e6-7596-4493-a297-a1515e5279d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c0e10e6-7596-4493-a297-a1515e5279d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cd35da2-413d-4d84-9616-62654c625c06' class='xr-var-data-in' type='checkbox'><label for='data-1cd35da2-413d-4d84-9616-62654c625c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2000, 1, 1, 0, 0, 0, 0),\n",
+       "        [4, 6]]])</pre></div></div></li><li class='xr-section-item'><input id='section-c35c3fd3-87a7-411e-914f-782bbe79dbe5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c35c3fd3-87a7-411e-914f-782bbe79dbe5' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2000-01-01 00:00:00 ... 2000-01-...</div><input id='attrs-dfaa345e-830f-4e8d-b4f9-bb50b79872d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfaa345e-830f-4e8d-b4f9-bb50b79872d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-316d07db-3006-429f-945d-81aa7ab41f8f' class='xr-var-data-in' type='checkbox'><label for='data-316d07db-3006-429f-945d-81aa7ab41f8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2000, 1, 1, 0, 0, 0, 0),\n",
        "       cftime.DatetimeGregorian(2000, 1, 2, 0, 0, 0, 0),\n",
-       "       cftime.DatetimeGregorian(2000, 1, 3, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observations_category_bounds</span></div><div class='xr-var-dims'>(observations_category)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;[0.0, 0.5)&#x27; &#x27;[0.5, 1.0)&#x27;</div><input id='attrs-5cbdc391-db9c-4dc0-9d56-eb0a2834abd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cbdc391-db9c-4dc0-9d56-eb0a2834abd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4afba08-f92f-4715-9f28-f590b6a0c562' class='xr-var-data-in' type='checkbox'><label for='data-f4afba08-f92f-4715-9f28-f590b6a0c562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;[0.0, 0.5)&#x27;, &#x27;[0.5, 1.0)&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecasts_category_bounds</span></div><div class='xr-var-dims'>(forecasts_category)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;[0.0, 0.5)&#x27; &#x27;[0.5, 1.0)&#x27;</div><input id='attrs-3030869a-8150-4b78-bd39-b6e409bc5a56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3030869a-8150-4b78-bd39-b6e409bc5a56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e287919b-a099-485e-bdf3-cc24ebda95de' class='xr-var-data-in' type='checkbox'><label for='data-e287919b-a099-485e-bdf3-cc24ebda95de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;[0.0, 0.5)&#x27;, &#x27;[0.5, 1.0)&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>observations_category</span></div><div class='xr-var-dims'>(observations_category)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-08535a52-886c-47e0-9e2c-8c48c338486c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08535a52-886c-47e0-9e2c-8c48c338486c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d70c292a-713a-4713-82d0-5590348ecb43' class='xr-var-data-in' type='checkbox'><label for='data-d70c292a-713a-4713-82d0-5590348ecb43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>forecasts_category</span></div><div class='xr-var-dims'>(forecasts_category)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-5e4a7aed-79fc-4a3c-9bbc-5a0b87ab7b79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e4a7aed-79fc-4a3c-9bbc-5a0b87ab7b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-966e7650-bc46-4b4e-a16f-a627d32df56e' class='xr-var-data-in' type='checkbox'><label for='data-966e7650-bc46-4b4e-a16f-a627d32df56e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47c0b62f-5183-4e9c-a440-49fc64960fcd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47c0b62f-5183-4e9c-a440-49fc64960fcd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       cftime.DatetimeGregorian(2000, 1, 3, 0, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observations_category_bounds</span></div><div class='xr-var-dims'>(observations_category)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;[0.0, 0.5)&#x27; &#x27;[0.5, 1.0)&#x27;</div><input id='attrs-7ef856d6-a147-4215-b7d5-9c6047ed9434' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ef856d6-a147-4215-b7d5-9c6047ed9434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49afeecf-266e-436a-b8cf-cf47096cdb05' class='xr-var-data-in' type='checkbox'><label for='data-49afeecf-266e-436a-b8cf-cf47096cdb05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;[0.0, 0.5)&#x27;, &#x27;[0.5, 1.0)&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecasts_category_bounds</span></div><div class='xr-var-dims'>(forecasts_category)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;[0.0, 0.5)&#x27; &#x27;[0.5, 1.0)&#x27;</div><input id='attrs-ef898ce3-d3b1-47ac-897e-725ee662786c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef898ce3-d3b1-47ac-897e-725ee662786c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5b107d-5a72-459f-a178-1bdc90d5cf82' class='xr-var-data-in' type='checkbox'><label for='data-cd5b107d-5a72-459f-a178-1bdc90d5cf82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;[0.0, 0.5)&#x27;, &#x27;[0.5, 1.0)&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>observations_category</span></div><div class='xr-var-dims'>(observations_category)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-e10deb4c-356a-4b6f-ba77-39a878609a71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e10deb4c-356a-4b6f-ba77-39a878609a71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4166046-9aff-4c48-b79b-efba11e21635' class='xr-var-data-in' type='checkbox'><label for='data-a4166046-9aff-4c48-b79b-efba11e21635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>forecasts_category</span></div><div class='xr-var-dims'>(forecasts_category)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-a9da7769-78a3-4757-90e3-4ed2d620b3a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9da7769-78a3-4757-90e3-4ed2d620b3a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-034c1b94-15cb-4bae-b2ed-d09078fdf64a' class='xr-var-data-in' type='checkbox'><label for='data-034c1b94-15cb-4bae-b2ed-d09078fdf64a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9faa234c-f130-4fdc-83e1-74d717cdb559' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9faa234c-f130-4fdc-83e1-74d717cdb559' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'histogram_observations_forecasts' (time: 3, observations_category: 2, forecasts_category: 2)>\n",
@@ -1387,14 +1387,6 @@
       "    alpha       float64 0.05\n",
       "    confidence  (time) float64 1.96 2.772 3.395 3.92 ... 19.3 19.4 19.5 19.6\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/aaron.spring/Coding/xskillscore/xskillscore/core/comparative.py:135: UserWarning: Ignoring provided observation because no metric was provided\n",
-      "  UserWarning,\n"
-     ]
     }
    ],
    "source": [
@@ -1411,7 +1403,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff77e367400>]"
+       "[<matplotlib.lines.Line2D at 0x7fec9731da30>]"
       ]
      },
      "execution_count": 37,
@@ -1420,7 +1412,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1537,7 +1529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.11"
+   "version": "3.8.6"
   }
  },
  "nbformat": 4,
diff --git a/xskillscore/core/comparative.py b/xskillscore/core/comparative.py
index 1cb724e..e2d30a1 100644
--- a/xskillscore/core/comparative.py
+++ b/xskillscore/core/comparative.py
@@ -3,6 +3,8 @@ import warnings
 import scipy.stats as st
 import xarray as xr
 
+from .utils import _add_as_coord
+
 
 def sign_test(
     forecasts1,
@@ -79,8 +81,8 @@ def sign_test(
     ...      coords=[('time', np.arange(30))])
     >>> st = sign_test(f1, f2, o, time_dim'time', metric='mae', orientation='negative')
     >>> st.plot()
-    >>> st['confidence'].plot(c='gray')
-    >>> (-1*st['confidence']).plot(c='gray')
+    >>> st['confidence'].plot(color='gray')
+    >>> (-1*st['confidence']).plot(color='gray')
 
     References
     ----------
@@ -152,16 +154,16 @@ def sign_test(
             metric_f1o = -metric_f1o
             metric_f2o = -metric_f2o
 
-    sign_test = (1 * (metric_f1o < metric_f2o) - 1 * (metric_f2o < metric_f1o)).cumsum(
+    walk = (1 * (metric_f1o < metric_f2o) - 1 * (metric_f2o < metric_f1o)).cumsum(
         time_dim
     )
 
-    # Estimate 95% confidence interval -----
+    # Estimate 1 - alpha confidence interval -----
     notnan = 1 * (metric_f1o.notnull() & metric_f2o.notnull())
     N = notnan.cumsum(time_dim)
     # z_alpha is the value at which the standardized cumulative Gaussian distributed
     # exceeds alpha
     confidence = st.norm.ppf(1 - alpha / 2) * xr.ufuncs.sqrt(N)
-    sign_test.coords["alpha"] = alpha
-    sign_test.coords["confidence"] = confidence
-    return sign_test
+    walk.coords["alpha"] = alpha
+    walk = _add_as_coord(walk, confidence, "confidence")
+    return walk

problem_statement: sign_test breaks with xr.Dataset
when running a first `sign_test` example in climpred, I find to minor modifications needed:
- currently fails for dataset inputs
- confidence has more dimensions than `time_dim`, not needed because confidence over all dimensions the same (maybe not if `NaN`s present?)
- change `c='gray'` to `color='gray'` in docstring
repo: xarray-contrib/xskillscore
test_cmd: pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
test_patch: diff --git a/xskillscore/tests/test_comparative.py b/xskillscore/tests/test_comparative.py
index f721591..9dd1ec0 100644
--- a/xskillscore/tests/test_comparative.py
+++ b/xskillscore/tests/test_comparative.py
@@ -1,4 +1,6 @@
+import numpy as np
 import pytest
+from dask import is_dask_collection
 
 import xskillscore as xs
 from xskillscore import sign_test
@@ -20,15 +22,31 @@ def logical(ds):
     return ds > 0.5
 
 
-def test_sign_test_raw(a_1d, a_1d_worse, b_1d):
-    """Test sign_test where significance crossed (for np.random.seed(42) values)."""
+@pytest.mark.parametrize("chunk", [True, False])
+@pytest.mark.parametrize("input", ["Dataset", "multidim Dataset", "DataArray", "mixed"])
+def test_sign_test_inputs(a_1d, a_1d_worse, b_1d, input, chunk):
+    """Test sign_test with xr inputs and chunked."""
+    if "Dataset" in input:
+        name = "var"
+        a_1d = a_1d.to_dataset(name=name)
+        a_1d_worse = a_1d_worse.to_dataset(name=name)
+        b_1d = b_1d.to_dataset(name=name)
+        if input == "multidim Dataset":
+            a_1d["var2"] = a_1d["var"] * 2
+            a_1d_worse["var2"] = a_1d_worse["var"] * 2
+            b_1d["var2"] = b_1d["var"] * 2
+    elif input == "mixed":
+        name = "var"
+        a_1d = a_1d.to_dataset(name=name)
+    if chunk:
+        a_1d = a_1d.chunk()
+        a_1d_worse = a_1d_worse.chunk()
+        b_1d = b_1d.chunk()
     actual = sign_test(
         a_1d, a_1d_worse, b_1d, time_dim="time", alpha=0.05, metric="mae"
     )
-    walk_larger_significance = actual > actual.confidence
-    crossing_after_timesteps = walk_larger_significance.argmax(dim="time")
-    # check timesteps after which sign_test larger confidence
-    assert crossing_after_timesteps == 3
+    # check dask collection preserved
+    assert is_dask_collection(actual) if chunk else not is_dask_collection(actual)
 
 
 @pytest.mark.parametrize("observation", [True, False])
@@ -183,7 +201,6 @@ def test_sign_test_dim(a, a_worse, b):
     assert len(actual.dims) == 1
 
 
-@pytest.mark.xfail()
 def test_sign_test_dim_fails(a_1d, a_1d_worse, b_1d):
     """Sign_test fails if no time_dim in dim."""
     with pytest.raises(ValueError) as e:
@@ -194,3 +211,12 @@ def test_sign_test_dim_fails(a_1d, a_1d_worse, b_1d):
 def test_sign_test_metric_correlation(a, a_worse, b):
     """Sign_test work for correlation metrics over other dimensions that time_dim."""
     sign_test(a, a_worse, b, time_dim="time", dim=["lon", "lat"], metric="pearson_r")
+
+
+def test_sign_test_NaNs_confidence(a, a_worse, b):
+    """Sign_test confidence with NaNs."""
+    actual = sign_test(a, a_worse, b, time_dim="time", metric="mse")
+    a_nan = a.copy()
+    a_nan[1:3, 1:3, 1:3] = np.nan
+    actual_nan = sign_test(a_nan, a_worse, b, time_dim="time", metric="mse")
+    assert not (actual_nan.confidence == actual.confidence).all()