diff --git a/stat/stat.go b/stat/stat.go
index b76907da..9bf24969 100644
--- a/stat/stat.go
+++ b/stat/stat.go
@@ -217,11 +217,14 @@ func Correlation(x, y, weights []float64) float64 {
 	return (sxy - xcompensation*ycompensation/sumWeights) / math.Sqrt(sxx*syy)
 }
 
-// KendallCorrelation returns the weighted Tau-a Kendall correlation between the
-// samples of x and y.
+// Kendall returns the weighted Tau-a Kendall correlation between the
+// samples of x and y. The Kendall correlation measures the quantity of
+// concordant and discordant pairs of numbers. If weights are specified then
+// each pair is weighted by weights[i] * weights[j] and the final sum is
+// normalized to stay between -1 and 1.
 // The lengths of x and y must be equal. If weights is nil then all of the
 // weights are 1. If weights is not nil, then len(x) must equal len(weights).
-func KendallCorrelation(x, y, weights []float64) float64 {
+func Kendall(x, y, weights []float64) float64 {
 	if len(x) != len(y) {
 		panic("stat: slice length mismatch")
 	}
@@ -250,15 +253,12 @@ func KendallCorrelation(x, y, weights []float64) float64 {
 
 	var sumWeights float64
 
-	var c int
-
 	for i := 0; i < n; i++ {
 		for j := i; j < n; j++ {
 			if i == j {
 				continue
 			}
-			c++
-			var weight = weights[i] * weights[j]
+			weight := weights[i] * weights[j]
 			if math.Signbit(x[j]-x[i]) == math.Signbit(y[j]-y[i]) {
 				cc += weight
 			} else {
diff --git a/stat/stat_test.go b/stat/stat_test.go
index e262c14c..bc5e3aee 100644
--- a/stat/stat_test.go
+++ b/stat/stat_test.go
@@ -136,7 +136,7 @@ func TestCorrelation(t *testing.T) {
 	}
 }
 
-func ExampleKendallCorrelation() {
+func ExampleKendall() {
 	x := []float64{8, -3, 7, 8, -4}
 	y := []float64{10, 5, 6, 3, -1}
 	w := []float64{2, 1.5, 3, 3, 2}
@@ -144,7 +144,7 @@ func ExampleKendallCorrelation() {
 	fmt.Println("Kendall correlation computes the number of ordered pairs")
 	fmt.Println("between two datasets.")
 
-	c := KendallCorrelation(x, y, w)
+	c := Kendall(x, y, w)
 	fmt.Printf("Kendall correlation is %.5f\n", c)
 
 	// Output:
@@ -153,7 +153,7 @@ func ExampleKendallCorrelation() {
 	// Kendall correlation is 0.25000
 }
 
-func TestKendallCorrelation(t *testing.T) {
+func TestKendall(t *testing.T) {
 	for i, test := range []struct {
 		x       []float64
 		y       []float64
@@ -197,19 +197,19 @@ func TestKendallCorrelation(t *testing.T) {
 			ans:     1,
 		},
 	} {
-		c := KendallCorrelation(test.x, test.y, test.weights)
+		c := Kendall(test.x, test.y, test.weights)
 		if math.Abs(test.ans-c) > 1e-14 {
 			t.Errorf("Correlation mismatch case %d. Expected %v, Found %v", i, test.ans, c)
 		}
 	}
-	if !panics(func() { KendallCorrelation(make([]float64, 2), make([]float64, 3), make([]float64, 3)) }) {
-		t.Errorf("KendallCorrelation did not panic with length mismatch")
+	if !panics(func() { Kendall(make([]float64, 2), make([]float64, 3), make([]float64, 3)) }) {
+		t.Errorf("Kendall did not panic with length mismatch")
 	}
-	if !panics(func() { KendallCorrelation(make([]float64, 2), make([]float64, 3), nil) }) {
-		t.Errorf("KendallCorrelation did not panic with length mismatch")
+	if !panics(func() { Kendall(make([]float64, 2), make([]float64, 3), nil) }) {
+		t.Errorf("Kendall did not panic with length mismatch")
 	}
-	if !panics(func() { KendallCorrelation(make([]float64, 3), make([]float64, 3), make([]float64, 2)) }) {
-		t.Errorf("KendallCorrelation did not panic with weights length mismatch")
+	if !panics(func() { Kendall(make([]float64, 3), make([]float64, 3), make([]float64, 2)) }) {
+		t.Errorf("Kendall did not panic with weights length mismatch")
 	}
 }