diff --git a/Statistics/Test/KruskalWallis.hs b/Statistics/Test/KruskalWallis.hs
index 3a91754..e15a04d 100644
--- a/Statistics/Test/KruskalWallis.hs
+++ b/Statistics/Test/KruskalWallis.hs
@@ -34,17 +34,22 @@ import qualified Statistics.Sample.Internal as Sample(sum)
 --
 -- The samples and values need not to be ordered but the values in the result
 -- are ordered. Assigned ranks (ties are given their average rank).
-kruskalWallisRank :: (U.Unbox a, Ord a) => [U.Vector a] -> [U.Vector Double]
-kruskalWallisRank samples = groupByTags
-                          . sortBy (comparing fst)
-                          . U.zip tags
-                          $ rank (==) joinSample
+kruskalWallisRank :: (U.Unbox a, Ord a) => [U.Vector a] -> Maybe [U.Vector Double]
+kruskalWallisRank samples = 
+  if firstRank == lastRank 
+    then Nothing
+    else Just 
+         . groupByTags
+         . sortBy (comparing fst)
+         . U.zip tags
+         $ rank (==) joinSample
   where
     (tags,joinSample) = U.unzip
                       . sortBy (comparing snd)
                       $ foldMap (uncurry tagSample) $ zip [(1::Int)..] samples
     tagSample t = U.map (\x -> (t,x))
-
+    firstRank = U.head joinSample
+    lastRank = U.last joinSample
     groupByTags xs
         | U.null xs = []
         | otherwise = sort (U.map snd ys) : groupByTags zs
@@ -56,21 +61,24 @@ kruskalWallisRank samples = groupByTags
 --
 -- In textbooks the output value is usually represented by 'K' or 'H'. This
 -- function already does the ranking.
-kruskalWallis :: (U.Unbox a, Ord a) => [U.Vector a] -> Double
-kruskalWallis samples = (nTot - 1) * numerator / denominator
-  where
-    -- Total number of elements in all samples
-    nTot    = fromIntegral $ sumWith rsamples U.length
-    -- Average rank of all samples
-    avgRank = (nTot + 1) / 2
-    --
-    numerator = sumWith rsamples $ \sample ->
-        let n = fromIntegral $ U.length sample
-        in  n * square (mean sample - avgRank)
-    denominator = sumWith rsamples $ \sample ->
-        Sample.sum $ U.map (\r -> square (r - avgRank)) sample
+kruskalWallis :: (U.Unbox a, Ord a) => [U.Vector a] -> Maybe Double
+kruskalWallis samples = case kruskalWallisRank samples of 
+  Nothing -> Nothing 
+  Just rsamples ->
+    Just $ (nTot - 1) * numerator / denominator
+      where
+        -- Total number of elements in all samples
+        nTot    = fromIntegral $ sumWith rsamples U.length
+        -- Average rank of all samples
+        avgRank = (nTot + 1) / 2
+        --
+        numerator = sumWith rsamples $ \sample ->
+            let n = fromIntegral $ U.length sample
+            in  n * square (mean sample - avgRank)
+        denominator = sumWith rsamples $ \sample ->
+            Sample.sum $ U.map (\r -> square (r - avgRank)) sample
+
 
-    rsamples = kruskalWallisRank samples
 
 
 -- | Perform Kruskal-Wallis Test for the given samples and required
@@ -80,18 +88,22 @@ kruskalWallis samples = (nTot - 1) * numerator / denominator
 -- It uses /Chi-Squared/ distribution for approximation as long as the sizes are
 -- larger than 5. Otherwise the test returns 'Nothing'.
 kruskalWallisTest :: (Ord a, U.Unbox a) => [U.Vector a] -> Maybe (Test ())
+-- If there are no samples, return Nothing since there's nothing to test.
 kruskalWallisTest []      = Nothing
-kruskalWallisTest samples
-  -- We use chi-squared approximation here
-  | all (>4) ns = Just Test { testSignificance = mkPValue $ complCumulative d k
-                            , testStatistics   = k
-                            , testDistribution = ()
-                            }
-  | otherwise   = Nothing
-  where
-    k  = kruskalWallis samples
-    ns = map U.length samples
-    d  = chiSquared (length ns - 1)
+-- If there is only one sample, return Nothing since there's nothing to test against.
+kruskalWallisTest [_]     = Nothing
+kruskalWallisTest samples = case kruskalWallis samples of 
+  Nothing -> Nothing
+  Just k -> 
+      -- We use chi-squared approximation here
+      if all (>4) ns then Just Test { testSignificance = mkPValue $ complCumulative d k
+                                , testStatistics   = k
+                                , testDistribution = ()
+                                }
+      else Nothing
+        where
+          ns = map U.length samples
+          d  = chiSquared (length ns - 1)
 
 -- * Helper functions