diff --git a/Project.toml b/Project.toml
index 5a010e9..a1bab56 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "NaNStatistics"
 uuid = "b946abbf-3ea7-4610-9019-9858bfdeaf2d"
 authors = ["C. Brenhin Keller"]
-version = "0.6.36"
+version = "0.6.37"
 
 [deps]
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
diff --git a/src/Histograms.jl b/src/Histograms.jl
index 6d3e7e1..09ccdca 100644
--- a/src/Histograms.jl
+++ b/src/Histograms.jl
@@ -35,6 +35,83 @@ function histcounts(x, xedges::AbstractRange; T=Int64)
 end
 histcounts(x, xmin::Number, xmax::Number, nbins::Integer; T=Int64) = histcounts(x, range(xmin, xmax, length=nbins+1); T=T)
 
+"""
+```julia
+histmean(counts, bincenters)
+```
+Estimate the mean of the data represented by a histogram, 
+specified as `counts` in equally spaced bins centered at `bincenters`.
+
+## Examples
+```julia
+julia> binedges = -10:0.01:10;
+
+julia> counts = histcounts(randn(10000), binedges);
+
+julia> bincenters = (binedges[1:end-1] + binedges[2:end])/2
+-9.995:0.01:9.995
+
+julia> histmean(counts, bincenters)
+0.0039890000000003135
+```
+"""
+function histmean(counts, bincenters)
+    N = ∅ₙ = zero(eltype(counts))
+    Σ = ∅ = zero(Base.promote_op(*, eltype(counts), eltype(bincenters)))
+    @inbounds @simd ivdep for i in eachindex(counts, bincenters)
+        pᵢ = counts[i] * bincenters[i]
+        Σ += ifelse(isnan(pᵢ), ∅, pᵢ)
+        N += ifelse(isnan(pᵢ), ∅ₙ, counts[i])
+    end
+    return Σ / N
+end
+export histmean
+
+"""
+```julia
+histstd(counts, bincenters; corrected::Bool=true)
+```
+Estimate the standard deviation of the data represented by a histogram, 
+specified as `counts` in equally spaced bins centered at `bincenters`.
+
+If `counts` have been normalized, or represent an analytical estimate of a PDF 
+rather than a histogram representing counts of a dataset, Bessel's correction 
+to the standard deviation should likely not be performed - i.e., set the 
+`corrected` keyword argument to `false`. 
+
+## Examples
+```julia
+julia> binedges = -10:0.01:10;
+
+julia> counts = histcounts(randn(10000), binedges);
+
+julia> bincenters = (binedges[1:end-1] + binedges[2:end])/2
+-9.995:0.01:9.995
+t
+julia> histstd(counts, bincenters)
+0.9991854064196424
+```
+"""
+function histstd(counts, bincenters; corrected::Bool=true)
+    N = ∅ₙ = zero(eltype(counts))
+    Σ = ∅ = zero(Base.promote_op(*, eltype(counts), eltype(bincenters)))
+    @inbounds @simd ivdep for i in eachindex(counts, bincenters)
+        pᵢ = counts[i] * bincenters[i]
+        Σ += ifelse(isnan(pᵢ), ∅, pᵢ)
+        N += ifelse(isnan(pᵢ), ∅ₙ, counts[i])
+    end
+    μ = Σ/N
+    Σ = ∅
+    @inbounds @simd ivdep for i in eachindex(counts, bincenters)
+        dᵢ = bincenters[i] - μ
+        pᵢ = counts[i] * dᵢ * dᵢ
+        Σ += ifelse(isnan(pᵢ), ∅, pᵢ)
+    end
+    return Σ / max(N-corrected, ∅ₙ)
+end
+export histstd
+
+
 
 """
 ```julia
diff --git a/test/Project.toml b/test/Project.toml
index 94bd924..6905ca2 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -1,4 +1,5 @@
 [deps]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
diff --git a/test/runtests.jl b/test/runtests.jl
index 54ed4eb..b976de6 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,5 +1,5 @@
 using NaNStatistics
-using Test, StatsBase, Statistics, LinearAlgebra
+using Test, StatsBase, Statistics, LinearAlgebra, Distributions
 
 @testset "ArrayStats" begin include("testArrayStats.jl") end
 @testset "Covariance and Correlation" begin include("testCovCor.jl") end
diff --git a/test/testHistograms.jl b/test/testHistograms.jl
index c175b08..53ed0be 100644
--- a/test/testHistograms.jl
+++ b/test/testHistograms.jl
@@ -34,5 +34,17 @@
     w = "size(N,2) < nxbins; any x bins beyond size(N,2) will not be filled"
     @test (@test_logs (:warn, w) histcounts!(zeros(10,5), x, y, xedges, yedges)) == I(10)[:,1:5]
 
+## --- Statistics on histograms
+
+    a = randn(10000)
+    binedges = -10:0.1:10
+    bincenters = (binedges[1:end-1] + binedges[2:end])/2
+    h = histcounts(a, binedges)
+    @test histmean(h, bincenters) ≈ nanmean(a) atol = 0.1
+    @test histstd(h, bincenters) ≈ nanstd(a) atol = 0.1
+
+    n = pdf.(Normal(0,1), bincenters)
+    @test histmean(n, bincenters) ≈ 0 atol = 1e-6
+    @test histstd(n, bincenters, corrected=false) ≈ 1 atol = 1e-6
 
 ## --- End of File