diff --git a/dist/norm.go b/dist/norm.go
index ed35acec..25c671ac 100644
--- a/dist/norm.go
+++ b/dist/norm.go
@@ -100,7 +100,7 @@ func (Normal) ExKurtosis() float64 {
 // Fit sets the parameters of the probability distribution from the
 // data samples x with relative weights w. If weights is nil, then all the weights
 // are 1. If weights is not nil, then the len(weights) must equal len(samples).
-func (n *Normal) Fit(samples []float64, weights []float64) {
+func (n *Normal) Fit(samples, weights []float64) {
 	suffStat := make([]float64, 1)
 	nSamples := n.SuffStat(samples, weights, suffStat)
 	n.ConjugateUpdate(suffStat, nSamples, []float64{0, 0})
diff --git a/stat.go b/stat.go
index 3cf3d665..7d019a47 100644
--- a/stat.go
+++ b/stat.go
@@ -672,7 +672,7 @@ func Mean(x, weights []float64) float64 {
 // Mode returns the most common value in the dataset specified by x and the
 // given weights. Strict float64 equality is used when comparing values, so users
 // should take caution. If several values are the mode, any of them may be returned.
-func Mode(x []float64, weights []float64) (val float64, count float64) {
+func Mode(x, weights []float64) (val float64, count float64) {
 	if weights != nil && len(x) != len(weights) {
 		panic("stat: slice length mismatch")
 	}
@@ -877,13 +877,13 @@ func (w weightSorter) Len() int {
 }
 
 // StdDev returns the sample standard deviation.
-func StdDev(x []float64, weights []float64) float64 {
+func StdDev(x, weights []float64) float64 {
 	_, std := MeanStdDev(x, weights)
 	return std
 }
 
 // MeanStdDev returns the sample mean and standard deviation
-func MeanStdDev(x []float64, weights []float64) (mean, std float64) {
+func MeanStdDev(x, weights []float64) (mean, std float64) {
 	mean, variance := MeanVariance(x, weights)
 	return mean, math.Sqrt(variance)
 }