diff --git a/src/t.jl b/src/t.jl
index e80eeef3..9e1aa75a 100644
--- a/src/t.jl
+++ b/src/t.jl
@@ -135,6 +135,25 @@ end
 testname(::EqualVarianceTTest) = "Two sample t-test (equal variance)"
 population_param_of_interest(x::TwoSampleTTest) = ("Mean difference", x.μ0, x.xbar) # parameter of interest: name, value under h0, point estimate
 
+"""
+    EqualVarianceTTest(nx::Int, ny::Int, mx::Real, my::Real, vx::Real, vy::Real, μ0::Real=0)
+
+Perform a two-sample t-test of the null hypothesis that samples `x` and `y` described by the number
+of elements `nx` and `ny`, the mean `mx` and `my`, and variance `vx` and `vy` come from distributions
+with equals means and variances. The alternative hypothesis is that the distributions have different
+means but equal variances.
+
+Implements: [`pvalue`](@ref), [`confint`](@ref)
+"""
+function EqualVarianceTTest(nx::Int, ny::Int, mx::Real, my::Real, vx::Real, vy::Real, μ0::Real=0)
+    xbar = mx - my
+    stddev = sqrt(((nx - 1) * vx + (ny - 1) * vy) / (nx + ny - 2))
+    stderr = stddev * sqrt(1/nx + 1/ny)
+    t = (xbar - μ0) / stderr
+    df = nx + ny - 2
+    EqualVarianceTTest(nx, ny, xbar, df, stderr, t, μ0)
+end
+
 """
     EqualVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
 
@@ -146,12 +165,9 @@ Implements: [`pvalue`](@ref), [`confint`](@ref)
 """
 function EqualVarianceTTest(x::AbstractVector{T}, y::AbstractVector{S}, μ0::Real=0) where {T<:Real,S<:Real}
     nx, ny = length(x), length(y)
-    xbar = mean(x) - mean(y)
-    stddev = sqrt(((nx - 1) * var(x) + (ny - 1) * var(y)) / (nx + ny - 2))
-    stderr = stddev * sqrt(1/nx + 1/ny)
-    t = (xbar - μ0) / stderr
-    df = nx + ny - 2
-    EqualVarianceTTest(nx, ny, xbar, df, stderr, t, μ0)
+    mx, my = mean(x), mean(y)
+    vx, vy = var(x), var(y)
+    EqualVarianceTTest(nx, ny, mx, my, vx, vy, μ0)
 end
 
 
diff --git a/test/t.jl b/test/t.jl
index 1f0ccb4b..bea30a7e 100644
--- a/test/t.jl
+++ b/test/t.jl
@@ -54,6 +54,18 @@ end
 	@test default_tail(tst) == :both
 	show(IOBuffer(), tst)
 
+	n1 = length(a1)
+	n2 = length(a2)
+	m1 = mean(a1)
+	m2 = mean(a2)
+	v1 = var(a1)
+	v2 = var(a2)
+
+	tst2 = EqualVarianceTTest(n1, n2, m1, m2, v1, v2)
+	@test tst.df == tst2.df
+	@test tst.t == tst2.t
+	@test pvalue(tst) == pvalue(tst2)
+
 	tst = UnequalVarianceTTest(a1, a2)
 	@test abs(tst.df - 7.03) <= 0.01
 	@test abs(tst.t - 1.959) <= 1e-3
@@ -61,5 +73,6 @@ end
 	@test all(abs.([confint(tst)...] - [-0.0196, 0.2096]) .<= 1e-4)
 	@test default_tail(tst) == :both
 	show(IOBuffer(), tst)
+
 end
 end