diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml
index 5115ac23..5724caa0 100644
--- a/.github/workflows/_test.yml
+++ b/.github/workflows/_test.yml
@@ -26,9 +26,7 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python.version }}
-      - name: Install the project
-        run: uv sync -p ${{ matrix.python.version }} -U --no-dev
+      - name: Install the project (include dev deps for tests)
+        run: uv sync -p ${{ matrix.python.version }} -U
       - name: Run pytest
-        run: >
-          uv run --with pytest --with pytest-cov
-          pytest --cov -m "not system"
+        run: uv run pytest -vv --cov -m "not system"
diff --git a/src/axtreme/eval/qoi_helpers.py b/src/axtreme/eval/qoi_helpers.py
index 4a7bdbe6..0e820043 100644
--- a/src/axtreme/eval/qoi_helpers.py
+++ b/src/axtreme/eval/qoi_helpers.py
@@ -40,7 +40,7 @@ def plot_distribution(
     for samples_i in samples_list:
         _ = ax.hist(samples_i, density=True, alpha=0.3, bins=len(samples_i) // 5 + 1)
     _ = ax.set_xlabel("QOI value")
-    _ = ax.set_title("QOi estimator distibutions")
+    _ = ax.set_title("QOI estimator distributions")
 
     if brute_force:
         _ = ax.axvline(brute_force, c="black", label=f"Brute force ({brute_force:.2f})")
diff --git a/src/axtreme/plotting/doe.py b/src/axtreme/plotting/doe.py
index a6bc6b57..4d2251f8 100644
--- a/src/axtreme/plotting/doe.py
+++ b/src/axtreme/plotting/doe.py
@@ -97,7 +97,7 @@ def plot_qoi_estimates_from_experiment(
         x,
         qoi_means - 1.96 * qoi_sems,
         qoi_means + 1.96 * qoi_sems,
-        label=f"90% Confidence Bound {name}",
+        label=f"95% Confidence Bound {name}",
         alpha=0.3,
         **kwargs,
     )
diff --git a/src/axtreme/plotting/gp_fit.py b/src/axtreme/plotting/gp_fit.py
index 9065acea..a91f2d18 100644
--- a/src/axtreme/plotting/gp_fit.py
+++ b/src/axtreme/plotting/gp_fit.py
@@ -40,10 +40,10 @@ def plot_surface_over_2d_search_space(
         colors: A list of colors to use for each function. If None, will use default Plotly colors.
         num_points: The number of points in each dimension to evaluate the functions at.
     """
-    # Extract the parameter names and ranges from the search space
+    # Extract the two parameters from the search space; only their ranges are used below.
     assert len(search_space.parameters) == 2, "Only 2D search spaces are supported for now."  # noqa: PLR2004
 
-    (x1_name, x1_param), (x2_name, x2_param) = list(search_space.parameters.items())
+    (_, x1_param), (_, x2_param) = list(search_space.parameters.items())
 
     if not (isinstance(x1_param, RangeParameter) and isinstance(x2_param, RangeParameter)):
         msg = f"""Expect search_space.parameters to all be of type RangeParameter.
@@ -371,11 +371,11 @@ def plot_1d_model(model: SingleTaskGP, X: torch.Tensor | None = None, ax: None |
         mean = posterior.mean[:, target_idx]
         var = posterior.variance[:, target_idx]
         _ = ax.fill_between(X.flatten(), mean - 1.95 * var**0.5, mean + 1.95 * var**0.5, alpha=0.3, color=c)
-        _ = ax.plot(X, mean, color=c, label=f"gp target {target_idx}")
+        _ = ax.plot(X, mean, color=c, label=f"GP target {target_idx}")
         _ = ax.scatter(train_x.flatten(), train_y, color=c)
         _ = ax.errorbar(train_x.flatten(), train_y, 1.95 * train_var**0.5, fmt="o", color=c)
 
-    _ = ax.set_title("Gp prediction")
+    _ = ax.set_title("GP prediction")
     return ax