diff --git a/src/multivariate/mvnormal.jl b/src/multivariate/mvnormal.jl
index 5806c00f31..76d68fd4f0 100644
--- a/src/multivariate/mvnormal.jl
+++ b/src/multivariate/mvnormal.jl
@@ -178,14 +178,9 @@ const ZeroMeanDiagNormal{Axes} = MvNormal{Float64,PDiagMat{Float64,Vector{Float6
 const ZeroMeanFullNormal{Axes} = MvNormal{Float64,PDMat{Float64,Matrix{Float64}},Zeros{Float64,1,Axes}}
 
 ### Construction
-function MvNormal(μ::AbstractVector{T}, Σ::AbstractPDMat{T}) where {T<:Real}
-    size(Σ, 1) == length(μ) || throw(DimensionMismatch("The dimensions of mu and Sigma are inconsistent."))
-    MvNormal{T,typeof(Σ), typeof(μ)}(μ, Σ)
-end
-
 function MvNormal(μ::AbstractVector{<:Real}, Σ::AbstractPDMat{<:Real})
-    R = Base.promote_eltype(μ, Σ)
-    MvNormal(convert(AbstractArray{R}, μ), convert(AbstractArray{R}, Σ))
+    size(Σ, 1) == length(μ) || throw(DimensionMismatch("The dimensions of mu and Sigma are inconsistent."))
+    return MvNormal{Base.promote_eltype(μ, Σ),typeof(Σ),typeof(μ)}(μ, Σ)
 end
 
 # constructor with general covariance matrix
@@ -197,8 +192,11 @@ Construct a multivariate normal distribution with mean `μ` and covariance matri
 MvNormal(μ::AbstractVector{<:Real}, Σ::AbstractMatrix{<:Real}) = MvNormal(μ, PDMat(Σ))
 MvNormal(μ::AbstractVector{<:Real}, Σ::Diagonal{<:Real}) = MvNormal(μ, PDiagMat(Σ.diag))
 MvNormal(μ::AbstractVector{<:Real}, Σ::Union{Symmetric{<:Real,<:Diagonal{<:Real}},Hermitian{<:Real,<:Diagonal{<:Real}}}) = MvNormal(μ, PDiagMat(Σ.data.diag))
-MvNormal(μ::AbstractVector{<:Real}, Σ::UniformScaling{<:Real}) =
-    MvNormal(μ, ScalMat(length(μ), Σ.λ))
+function MvNormal(μ::AbstractVector{<:Real}, Σ::UniformScaling{<:Real})
+    # Promote `Bool` (`I`) to avoid surprising covariance element types
+    λ = Σ isa UniformScaling{Bool} ? promote_type(eltype(μ), Bool)(Σ.λ) : Σ.λ
+    return MvNormal(μ, ScalMat(length(μ), λ))
+end
 function MvNormal(
     μ::AbstractVector{<:Real}, Σ::Diagonal{<:Real,<:FillArrays.AbstractFill{<:Real,1}}
 )
diff --git a/test/multivariate/mvnormal.jl b/test/multivariate/mvnormal.jl
index 4af9275b3f..604064931d 100644
--- a/test/multivariate/mvnormal.jl
+++ b/test/multivariate/mvnormal.jl
@@ -82,9 +82,9 @@ end
     C = [4. -2. -1.; -2. 5. -1.; -1. -1. 6.]
     J = inv(C)
     h = J \ mu
-    @test typeof(MvNormal(mu, PDMat(Array{Float32}(C)))) == typeof(MvNormal(mu, PDMat(C)))
-    @test typeof(MvNormal(mu, Array{Float32}(C))) == typeof(MvNormal(mu, PDMat(C)))
-    @test typeof(@test_deprecated(MvNormal(mu, 2.0f0))) == typeof(@test_deprecated(MvNormal(mu, 2.0)))
+    @test MvNormal(mu, PDMat(Array{Float32}(C))) isa MvNormal{Float64, PDMat{Float32, Matrix{Float32}}, Vector{Float64}}
+    @test MvNormal(mu, Array{Float32}(C)) isa MvNormal{Float64, PDMat{Float32, Matrix{Float32}}, Vector{Float64}}
+    @test @test_deprecated(MvNormal(mu, 2.0f0)) isa MvNormal{Float64, ScalMat{Float32}, Vector{Float64}}
 
     @test typeof(MvNormalCanon(h, PDMat(Array{Float32}(J)))) == typeof(MvNormalCanon(h, PDMat(J)))
     @test typeof(MvNormalCanon(h, Array{Float32}(J))) == typeof(MvNormalCanon(h, PDMat(J)))
@@ -102,9 +102,9 @@ end
     @test typeof(convert(MvNormalCanon{Float64}, d)) == typeof(MvNormalCanon(mu, h, PDMat(J)))
     @test typeof(convert(MvNormalCanon{Float64}, d.μ, d.h, d.J)) == typeof(MvNormalCanon(mu, h, PDMat(J)))
 
-    @test MvNormal(mu, I) === @test_deprecated(MvNormal(mu, 1))
+    @test MvNormal(mu, I) === @test_deprecated(MvNormal(mu, 1.0))
     @test MvNormal(mu, 9 * I) === @test_deprecated(MvNormal(mu, 3))
-    @test MvNormal(mu, 0.25f0 * I) === @test_deprecated(MvNormal(mu, 0.5))
+    @test MvNormal(mu, 0.25f0 * I) === @test_deprecated(MvNormal(mu, 0.5f0))
 
     @test MvNormal(mu, I) === MvNormal(mu, Diagonal(Ones(length(mu))))
     @test MvNormal(mu, 9 * I) === MvNormal(mu, Diagonal(Fill(9, length(mu))))