diff --git a/src/Numerics/Statistics/ArrayStatistics.cs b/src/Numerics/Statistics/ArrayStatistics.cs
index f338eb5e..28418d07 100644
--- a/src/Numerics/Statistics/ArrayStatistics.cs
+++ b/src/Numerics/Statistics/ArrayStatistics.cs
@@ -128,17 +128,6 @@ namespace MathNet.Numerics.Statistics
             return variance/(samples.Length - 1);
         }
 
-        /// <summary>
-        /// Estimates the unbiased population standard deviation from the provided samples as unsorted array.
-        /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
-        /// Returns NaN if data has less than two entries or if any entry is NaN.
-        /// </summary>
-        /// <param name="samples">Sample array, no sorting is assumed.</param>
-        public static double StandardDeviation(double[] samples)
-        {
-            return Math.Sqrt(Variance(samples));
-        }
-
         /// <summary>
         /// Evaluates the population variance from the full population provided as unsorted array.
         /// On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
@@ -161,6 +150,17 @@ namespace MathNet.Numerics.Statistics
             return variance/population.Length;
         }
 
+        /// <summary>
+        /// Estimates the unbiased population standard deviation from the provided samples as unsorted array.
+        /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
+        /// Returns NaN if data has less than two entries or if any entry is NaN.
+        /// </summary>
+        /// <param name="samples">Sample array, no sorting is assumed.</param>
+        public static double StandardDeviation(double[] samples)
+        {
+            return Math.Sqrt(Variance(samples));
+        }
+
         /// <summary>
         /// Evaluates the population standard deviation from the full population provided as unsorted array.
         /// On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
diff --git a/src/Numerics/Statistics/StreamingStatistics.cs b/src/Numerics/Statistics/StreamingStatistics.cs
index 6be6d5cb..5ee9c53c 100644
--- a/src/Numerics/Statistics/StreamingStatistics.cs
+++ b/src/Numerics/Statistics/StreamingStatistics.cs
@@ -140,17 +140,6 @@ namespace MathNet.Numerics.Statistics
             return j > 1 ? variance/(j - 1) : double.NaN;
         }
 
-        /// <summary>
-        /// Estimates the unbiased population standard deviation from the provided samples as enumerable sequence, in a single pass without memoization.
-        /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
-        /// Returns NaN if data has less than two entries or if any entry is NaN.
-        /// </summary>
-        /// <param name="samples">Sample stream, no sorting is assumed.</param>
-        public static double StandardDeviation(IEnumerable<double> samples)
-        {
-            return Math.Sqrt(Variance(samples));
-        }
-
         /// <summary>
         /// Evaluates the population variance from the full population provided as enumerable sequence, in a single pass without memoization.
         /// On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.
@@ -184,6 +173,17 @@ namespace MathNet.Numerics.Statistics
             return variance/j;
         }
 
+        /// <summary>
+        /// Estimates the unbiased population standard deviation from the provided samples as enumerable sequence, in a single pass without memoization.
+        /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
+        /// Returns NaN if data has less than two entries or if any entry is NaN.
+        /// </summary>
+        /// <param name="samples">Sample stream, no sorting is assumed.</param>
+        public static double StandardDeviation(IEnumerable<double> samples)
+        {
+            return Math.Sqrt(Variance(samples));
+        }
+
         /// <summary>
         /// Evaluates the population standard deviation from the full population provided as enumerable sequence, in a single pass without memoization.
         /// On a dataset of size N will use an N normalizer and would thus be biased if applied to a subset.