diff --git a/scripts/dwi_gp_estimation_error_analysis.py b/scripts/dwi_gp_estimation_error_analysis.py
index eb393b97..a0616493 100644
--- a/scripts/dwi_gp_estimation_error_analysis.py
+++ b/scripts/dwi_gp_estimation_error_analysis.py
@@ -31,6 +31,7 @@
 import argparse
 from collections import defaultdict
 from pathlib import Path
+from typing import DefaultDict, List
 
 import numpy as np
 import pandas as pd
@@ -49,7 +50,7 @@ def cross_validate(
     cv: int,
     n_repeats: int,
     gpr: DiffusionGPR,
-) -> dict[int, list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]]:
+) -> np.ndarray:
     """
     Perform the experiment by estimating the dMRI signal using a Gaussian process model.
 
@@ -68,7 +69,7 @@ def cross_validate(
 
     Returns
     -------
-    :obj:`dict`
+    :obj:`~numpy.ndarray`
         Data for the predicted signal and its error.
 
     """
@@ -202,12 +203,14 @@ def main() -> None:
         # max_iter=2e5,
     )
 
+    n_repeats = 10
+
     if args.kfold:
         # Use Scikit-learn cross validation
-        scores = defaultdict(list, {})
+        scores: DefaultDict[str, List[float | str]] = defaultdict(list)
         for n in args.kfold:
             for i in range(args.repeats):
-                cv_scores = -1.0 * cross_validate(X, y.T, n, gpr)
+                cv_scores = -1.0 * cross_validate(X, y.T, n, n_repeats, gpr)
                 scores["rmse"] += cv_scores.tolist()
                 scores["repeat"] += [i] * len(cv_scores)
                 scores["n_folds"] += [n] * len(cv_scores)
@@ -217,7 +220,7 @@ def main() -> None:
             print(f"Finished {n}-fold cross-validation")
 
         scores_df = pd.DataFrame(scores)
-        scores_df.to_csv(args.output_scores, sep="\t", index=None, na_rep="n/a")
+        scores_df.to_csv(args.output_scores, sep="\t", index=False, na_rep="n/a")
 
         grouped = scores_df.groupby(["n_folds"])
         print(grouped[["rmse"]].mean())