diff --git a/README.md b/README.md
index 76658fd..f32e578 100644
--- a/README.md
+++ b/README.md
@@ -365,7 +365,7 @@ with open_state("example.asreview") as s:
 ![Recall with absolute
 axes](https://github.com/asreview/asreview-insights/blob/main/docs/example_absolute_axes.png)
 
-#### Example: Adjusting the random and optimal curves
+#### Example: Adjusting the random and optimal recalls
 
 By default, each plot will have a curve representing optimal performance, and a
 curve representing random sampling performance. Both curves can be removed from
diff --git a/asreviewcontrib/insights/plot.py b/asreviewcontrib/insights/plot.py
index 93c4f23..2ee70eb 100644
--- a/asreviewcontrib/insights/plot.py
+++ b/asreviewcontrib/insights/plot.py
@@ -36,7 +36,7 @@ def plot_recall(
     show_random: bool
         Show the random curve in the plot.
     show_optimal: bool
-        Show the optimal curve in the plot.
+        Show the optimal recall in the plot.
     show_legend: bool
         If state_obj contains multiple states, show a legend in the plot.
     legend_values: list[str]
@@ -407,12 +407,12 @@ def _add_random_curve(ax, labels, x_absolute, y_absolute):
 
 
 def _add_optimal_curve(ax, labels, x_absolute, y_absolute):
-    """Add a optimal curve to a plot using step-wise increments.
+    """Add a optimal recall to a plot using step-wise increments.
 
     Returns
     -------
     plt.axes.Axes
-        Axes with optimal curve added.
+        Axes with optimal recall added.
     """
     # get total amount of positive labels
     if isinstance(labels[0], list):
@@ -434,7 +434,7 @@ def _add_optimal_curve(ax, labels, x_absolute, y_absolute):
         else np.arange(0, n_pos_docs + 1) / n_pos_docs
     )
 
-    # Plot the stepwise optimal curve
+    # Plot the stepwise optimal recall
     ax.step(x, y, color="grey", where="post")
 
     return ax
diff --git a/docs/example_multiple_lines.py b/docs/example_multiple_lines.py
index f38936a..4b5ad44 100644
--- a/docs/example_multiple_lines.py
+++ b/docs/example_multiple_lines.py
@@ -19,7 +19,7 @@
     plot_recall(ax, s2)
 
 # Set the labels for the legend. Both plots add the recall line, the random
-# curve, and the optimal curve. Hence the recall lines are the 0th and 3nd line.
+# curve, and the optimal recall. Hence the recall lines are the 0th and 3nd line.
 ax.lines[0].set_label("Naive Bayes")
 ax.lines[3].set_label("Logistic")
 ax.legend()