diff --git a/src/scalarstats.jl b/src/scalarstats.jl
index 924d681cf..1e4406c8a 100644
--- a/src/scalarstats.jl
+++ b/src/scalarstats.jl
@@ -261,36 +261,101 @@ realXcY(x::Real, y::Real) = x*y
 realXcY(x::Complex, y::Complex) = real(x)*real(y) + imag(x)*imag(y)
 
 """
-    sem(x)
+    sem(x; mean=nothing)
+    sem(x::AbstractArray[, weights::AbstractWeights]; mean=nothing)
 
-Return the standard error of the mean of collection `x`,
-i.e. `sqrt(var(x, corrected=true) / length(x))`.
+Return the standard error of the mean for a collection `x`.
+A pre-computed `mean` may be provided.
+
+When not using weights, this is the (sample) standard deviation
+divided by the sample size. If weights are used, the 
+variance of the sample mean is calculated as follows:
+
+* `AnalyticWeights`: Not implemented.
+* `FrequencyWeights`: ``\\frac{\\sum_{i=1}^n w_i (x_i - \\bar{x_i})^2}{(\\sum w_i) (\\sum w_i - 1)}``
+* `ProbabilityWeights`: ``\\frac{n}{n-1} \\frac{\\sum_{i=1}^n w_i^2 (x_i - \\bar{x_i})^2}{\\left( \\sum w_i \\right)^2}``
+
+The standard error is then the square root of the above quantities.
+
+# References
+
+Carl-Erik Särndal, Bengt Swensson, Jan Wretman (1992). Model Assisted Survey Sampling.
+New York: Springer. pp. 51-53.
 """
-function sem(x)
-    y = iterate(x)
-    if y === nothing
+function sem(x; mean=nothing)
+    if isempty(x)
+        # Return the NaN of the type that we would get for a nonempty x
         T = eltype(x)
-        # Return the NaN of the type that we would get, had this collection
-        # contained any elements (this is consistent with std)
-        return oftype(sqrt((abs2(zero(T)) + abs2(zero(T)))/2), NaN)
-    end
-    count = 1
-    value, state = y
-    y = iterate(x, state)
-    # Use Welford algorithm as seen in (among other places)
-    # Knuth's TAOCP, Vol 2, page 232, 3rd edition.
-    M = value / 1
-    S = real(zero(M))
-    while y !== nothing
+        _mean = mean === nothing ? zero(T) / 1 : mean
+        z = abs2(zero(T) - _mean)
+        return oftype((z + z) / 2, NaN)
+    elseif mean === nothing
+        n = 0
+        y = iterate(x)
+        value, state = y
+        # Use Welford algorithm as seen in (among other places)
+        # Knuth's TAOCP, Vol 2, page 232, 3rd edition.
+        _mean = value / 1
+        sse = real(zero(_mean))
+        while y !== nothing
+            value, state = y
+            y = iterate(x, state)
+            n += 1
+            new_mean = _mean + (value - _mean) / n
+            sse += realXcY(value - _mean, value - new_mean)
+            _mean = new_mean
+        end
+    else
+        n = 1
+        y = iterate(x)
         value, state = y
-        y = iterate(x, state)
-        count += 1
-        new_M = M + (value - M) / count
-        S = S + realXcY(value - M, value - new_M)
-        M = new_M
+        sse = abs2(value - mean)
+        while (y = iterate(x, state)) !== nothing
+            value, state = y
+            n += 1
+            sse += abs2(value - mean)
+        end
+    end
+    variance = sse / (n - 1)
+    return sqrt(variance / n)
+end
+
+function sem(x::AbstractArray; mean=nothing) 
+    if isempty(x)
+        # Return the NaN of the type that we would get for a nonempty x
+        T = eltype(x)
+        _mean = mean === nothing ? zero(T) / 1 : mean
+        z = abs2(zero(T) - _mean)
+        return oftype((z + z) / 2, NaN)
+    end
+    return sqrt(var(x; mean=mean, corrected=true) / length(x))
+end
+
+function sem(x::AbstractArray, weights::UnitWeights; mean=nothing)
+    if length(x) ≠ length(weights)
+        throw(DimensionMismatch("array and weights do not have the same length"))
+    end
+    return sem(x; mean=mean)
+end
+
+
+# Weighted methods for the above
+sem(x::AbstractArray, weights::FrequencyWeights; mean=nothing) =
+    sqrt(var(x, weights; mean=mean, corrected=true) / sum(weights))
+
+function sem(x::AbstractArray, weights::ProbabilityWeights; mean=nothing)
+    if isempty(x)
+        # Return the NaN of the type that we would get for a nonempty x
+        return var(x, weights; mean=mean, corrected=true) / 0
+    else
+        _mean = mean === nothing ? Statistics.mean(x, weights) : mean
+        # sum of squared errors = sse
+        sse = sum(Broadcast.instantiate(Broadcast.broadcasted(x, weights) do x_i, w
+            return abs2(w * (x_i - _mean))
+        end))
+        n = count(!iszero, weights)
+        return sqrt(sse * n / (n - 1)) / sum(weights)
     end
-    var = S / (count - 1)
-    return sqrt(var/count)
 end
 
 # Median absolute deviation
diff --git a/test/scalarstats.jl b/test/scalarstats.jl
index 6b561a130..4d5054918 100644
--- a/test/scalarstats.jl
+++ b/test/scalarstats.jl
@@ -107,10 +107,36 @@ z2 = [8. 2. 3. 1.; 24. 10. -1. -1.; 20. 12. 1. -2.]
 @test variation([1:5;]) ≈ 0.527046276694730
 @test variation(skipmissing([missing; 1:5; missing])) ≈ 0.527046276694730
 
-@test sem([1:5;]) ≈ 0.707106781186548
-@test sem(skipmissing([missing; 1:5; missing])) ≈ 0.707106781186548
-@test sem(Int[]) === NaN
-@test sem(skipmissing(Union{Int,Missing}[missing, missing])) === NaN
+@test @inferred(sem([1:5;])) ≈ 0.707106781186548
+@test @inferred(sem(skipmissing([missing; 1:5; missing]))) ≈ 0.707106781186548
+@test @inferred(sem(skipmissing([missing; 1:5; missing]), mean=3.0)) ≈ 0.707106781186548
+@test @inferred(sem([1:5;], UnitWeights{Int}(5))) ≈ 0.707106781186548
+@test @inferred(sem([1:5;], UnitWeights{Int}(5); mean=mean(1:5))) ≈ 0.707106781186548
+@test_throws DimensionMismatch sem(1:5, UnitWeights{Int}(4))
+@test @inferred(sem([1:5;], ProbabilityWeights([1:5;]))) ≈ 0.6166 rtol=.001
+μ = mean(1:5, ProbabilityWeights([1:5;]))
+@test @inferred(sem([1:5;], ProbabilityWeights([1:5;]); mean=μ)) ≈ 0.6166 rtol=.001
+@test @inferred(sem([10; 1:5;], ProbabilityWeights([0; 1:5;]); mean=μ)) ≈ 0.6166 rtol=.001
+x = sort!(vcat([5:-1:i for i in 1:5]...))
+μ = mean(x)
+@test @inferred(sem([1:5;], FrequencyWeights([1:5;]))) ≈ sem(x)
+@test @inferred(sem([1:5;], FrequencyWeights([1:5;]); mean=μ)) ≈ sem(x)
+
+@inferred sem([1:5f0;]; mean=μ) ≈ sem(x)
+@inferred sem([1:5f0;], ProbabilityWeights([1:5;]); mean=μ)
+@inferred sem([1:5f0;], FrequencyWeights([1:5;]); mean=μ)
+# Broken: Bug to do with Statistics.jl's implementation of `var`
+# @inferred sem([1:5f0;], UnitWeights{Int}(5); mean=μ)
+
+@test @inferred(isnan(sem(Int[])))
+@test @inferred(isnan(sem(Int[], FrequencyWeights(Int[]))))
+@test @inferred(isnan(sem(Int[], ProbabilityWeights(Int[]))))
+
+@test @inferred(isnan(sem(Int[]; mean=0f0)))
+@test @inferred(isnan(sem(Int[], FrequencyWeights(Int[]); mean=0f0)))
+@test @inferred(isnan(sem(Int[], ProbabilityWeights(Int[]); mean=0f0)))
+
+@test @inferred(isnan(sem(skipmissing(Union{Int,Missing}[missing, missing]))))
 @test_throws MethodError sem(Any[])
 @test_throws MethodError sem(skipmissing([missing]))