diff --git a/examples/tree_1d_dgsem/elixir_advection_uncertainty.jl b/examples/tree_1d_dgsem/elixir_advection_uncertainty.jl
new file mode 100644
index 00000000000..8796d03b094
--- /dev/null
+++ b/examples/tree_1d_dgsem/elixir_advection_uncertainty.jl
@@ -0,0 +1,31 @@
+using Trixi
+using OrdinaryDiffEqLowStorageRK
+using Measurements # For propagating uncertainty/measurement errors in parameters
+
+# Note the `±` operator for defining uncertain parameters
+equations = LinearScalarAdvectionEquation1D(1.0 ± 0.1)
+
+x_min = (-1.0,)
+x_max = (1.0,)
+mesh = TreeMesh(x_min, x_max,
+                n_cells_max = 10^5, initial_refinement_level = 5)
+
+solver = DGSEM(polydeg = 3, surface_flux = flux_lax_friedrichs)
+
+RealT = Measurement{Float64} # Measurement datatype
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test,
+                                    solver, uEltype = RealT)
+
+tspan = (0.0, 1.5)
+ode = semidiscretize(semi, tspan)
+
+summary_callback = SummaryCallback()
+analysis_callback = AnalysisCallback(semi, interval = 50)
+
+callbacks = CallbackSet(summary_callback, analysis_callback)
+
+###############################################################################
+# run the simulation
+
+sol = solve(ode, RDPK3SpFSAL49();
+            ode_default_options()..., callback = callbacks);
diff --git a/test/Project.toml b/test/Project.toml
index c996ec756a5..6111a9c6e61 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -12,6 +12,7 @@ ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
 MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
 NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 OrdinaryDiffEqFeagin = "101fe9f7-ebb6-4678-b671-3a81e7194747"
@@ -43,6 +44,7 @@ ECOS = "1.1.2"
 ExplicitImports = "1.0.1"
 ForwardDiff = "0.10.36, 1"
 LinearAlgebra = "1"
+Measurements = "2.14.0"
 MPI = "0.20.6"
 NLsolve = "4.5.1"
 OrdinaryDiffEqFeagin = "1"
diff --git a/test/test_tree_1d_advection.jl b/test/test_tree_1d_advection.jl
index ebc78c063e8..b1644b9d0b4 100644
--- a/test/test_tree_1d_advection.jl
+++ b/test/test_tree_1d_advection.jl
@@ -169,6 +169,23 @@ end
         @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 1000
     end
 end
+
+@trixi_testset "elixir_advection_uncertainty.jl" begin
+    # Run this first to collect LoopVectorization warnings before error-check run
+    @test_nowarn_mod trixi_include(joinpath(examples_dir(), "tree_1d_dgsem",
+                                            "elixir_advection_uncertainty.jl"))
+
+    @test_trixi_include(joinpath(EXAMPLES_DIR, "elixir_advection_uncertainty.jl"),
+                        l2=[0.0012576893000440965 ± 0.017581020765034417],
+                        linf=[0.004425204509676317 ± 0.0633672486044246])
+    # Using Measurements.jl actually allocates quite significantly
+    let
+        t = sol.t[end]
+        u_ode = sol.u[end]
+        du_ode = similar(u_ode)
+        @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 100_000
+    end
+end
 end
 
 end # module
diff --git a/test/test_trixi.jl b/test/test_trixi.jl
index 00baf2162d0..d20a325cb8c 100644
--- a/test/test_trixi.jl
+++ b/test/test_trixi.jl
@@ -120,7 +120,7 @@ macro test_nowarn_mod(expr, additional_ignore_content = [])
             # Warnings from third party packages
             r"┌ Warning: Problem status ALMOST_INFEASIBLE; solution may be inaccurate.\n└ @ Convex ~/.julia/packages/Convex/.*\n",
             r"┌ Warning: Problem status ALMOST_OPTIMAL; solution may be inaccurate.\n└ @ Convex ~/.julia/packages/Convex/.*\n",
-            # Warnings for higher-precision floating data types
+            # Warnings for higher-precision floating data types or Measurements.jl
             r"┌ Warning: #= /home/runner/work/Trixi.jl/Trixi.jl/src/solvers/dgsem/interpolation.jl:118 =#:\n│ `LoopVectorization.check_args` on your inputs failed; running fallback `@inbounds @fastmath` loop instead.\n│ Use `warn_check_args=false`, e.g. `@turbo warn_check_args=false ...`, to disable this warning.\n└ @ Trixi ~/.julia/packages/LoopVectorization/.*\n",
             r"┌ Warning: #= /home/runner/work/Trixi.jl/Trixi.jl/src/solvers/dgsem/interpolation.jl:136 =#:\n│ `LoopVectorization.check_args` on your inputs failed; running fallback `@inbounds @fastmath` loop instead.\n│ Use `warn_check_args=false`, e.g. `@turbo warn_check_args=false ...`, to disable this warning.\n└ @ Trixi ~/.julia/packages/LoopVectorization/.*\n"
         ]