diff --git a/test/multi_choice_models/ddm_tests.jl b/test/multi_choice_models/ddm_tests.jl
index b0e59df7..afc3b06d 100644
--- a/test/multi_choice_models/ddm_tests.jl
+++ b/test/multi_choice_models/ddm_tests.jl
@@ -331,11 +331,11 @@
 
         # Gradients stay finite even when pdf is too small to differentiate reliably.
         for (ν, α, z, τ, rt) ∈ [
-            (6.0, 6.0, 0.2, 0.2, 0.201),
-            (5.0, 4.0, 0.95, 0.05, 0.06),
-            (4.0, 1.5, 0.8, 0.2, 0.201),
-            (6.0, 0.3, 0.05, 0.05, 10.05)
-        ],
+                (6.0, 6.0, 0.2, 0.2, 0.201),
+                (5.0, 4.0, 0.95, 0.05, 0.06),
+                (4.0, 1.5, 0.8, 0.2, 0.201),
+                (6.0, 0.3, 0.05, 0.05, 10.05)
+            ],
             choice ∈ (1, 2)
 
             grad = ForwardDiff.gradient(p -> f(p, choice, rt), [ν, α, z, τ])