diff --git a/test/runtests.jl b/test/runtests.jl
index f374d5bc..e8758a70 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -170,7 +170,7 @@ anorexia = CSV.read(joinpath(glm_datadir, "anorexia.csv"))
     @test isapprox(coef(gm6),
         [45.674043486911685, -0.5655388496390964, 4.097065528072901, 8.660128180991693])
     @test isapprox(GLM.dispersion(gm6.model, true), 48.6950385282296)
-    @test isapprox(stderr(gm6),
+    @test isapprox(StatsBase.stderr(gm6),
         [13.21670540145523, 0.16118236185182783, 1.893492606966926, 2.1931494116430517])
 end
 
@@ -183,7 +183,7 @@ end
     @test isapprox(coef(gm7),
         [3.9416285291318798, -0.9944526931311773, 0.050698258703983666, 0.1021922886616272])
     @test isapprox(GLM.dispersion(gm7.model, true), 48.017753573192266)
-    @test isapprox(stderr(gm7), 
+    @test isapprox(StatsBase.stderr(gm7), 
         [0.1554026019351794, 0.0018862835443589627, 0.022584040191142126, 0.025187228659634627],
         atol=1e-6)
 end
@@ -205,7 +205,7 @@ clotting = DataFrame(u = log.([5,10,15,20,30,40,60,80,100]),
     @test isapprox(bic(gm8), 38.58159768156315)
     @test isapprox(coef(gm8), [-0.01655438172784895,0.01534311491072141])
     @test isapprox(GLM.dispersion(gm8.model, true), 0.002446059333495581, atol=1e-6)
-    @test isapprox(stderr(gm8), [0.00092754223, 0.000414957683], atol=1e-6)
+    @test isapprox(StatsBase.stderr(gm8), [0.00092754223, 0.000414957683], atol=1e-6)
 end
 
 @testset "InverseGaussian" begin
@@ -221,7 +221,7 @@ end
     @test isapprox(bic(gm8a), 62.16652574970839)
     @test isapprox(coef(gm8a), [-0.0011079770504295668,0.0007219138982289362])
     @test isapprox(GLM.dispersion(gm8a.model, true), 0.0011008719709455776, atol=1e-6)
-    @test isapprox(stderr(gm8a), [0.0001675339726910311,9.468485015919463e-5], atol=1e-6)
+    @test isapprox(StatsBase.stderr(gm8a), [0.0001675339726910311,9.468485015919463e-5], atol=1e-6)
 end
 
 @testset "Gamma LogLink" begin
@@ -237,7 +237,7 @@ end
     @test isapprox(bic(gm9), 59.07332993970688)
     @test isapprox(coef(gm9), [5.50322528458221, -0.60191617825971])
     @test isapprox(GLM.dispersion(gm9.model, true), 0.02435442293561081)
-    @test isapprox(stderr(gm9), [0.19030107482720, 0.05530784660144])
+    @test isapprox(StatsBase.stderr(gm9), [0.19030107482720, 0.05530784660144])
 end
 
 @testset "Gamma IdentityLink" begin
@@ -253,7 +253,7 @@ end
     @test isapprox(bic(gm10), 71.02381860657701)
     @test isapprox(coef(gm10), [99.250446880986, -18.374324929002])
     @test isapprox(GLM.dispersion(gm10.model, true), 0.10417373, atol=1e-6)
-    @test isapprox(stderr(gm10), [17.864084, 4.297895], atol=1e-4)
+    @test isapprox(StatsBase.stderr(gm10), [17.864084, 4.297895], atol=1e-4)
 end
 
 # Logistic regression using aggregated data and weights