diff --git a/src/signature_mahalanobis_knn/sig_mahal_knn.py b/src/signature_mahalanobis_knn/sig_mahal_knn.py
index dc88996..d831460 100644
--- a/src/signature_mahalanobis_knn/sig_mahal_knn.py
+++ b/src/signature_mahalanobis_knn/sig_mahal_knn.py
@@ -181,6 +181,7 @@ def conformance(
         signatures_test: np.ndarray | None = None,
         n_neighbors: int = 20,
         return_indices: bool = False,
+        take_median: bool = False,
     ) -> np.ndarray:
         """
         Compute the conformance scores for the data points either passed in
@@ -202,9 +203,14 @@ def conformance(
         signatures_test : np.ndarray | None, optional
             Signatures of the data points, by default None.
             Two dimensional array of shape (n_samples, sig_dim).
+        n_neighbors : int, optional
         return_indices : bool, optional
             Whether to return the indices of the nearest neighbors,
             by default False.
+        take_median : bool, optional
+            Whether we should take the median distance of the k nearest neighbours.
+            By default, the pipeline takes the minimum distance. (min of k nearest neighbours = closest neighbour)
+            Setting take_median to true allows for a more robust measure.
 
         Returns
         -------
@@ -274,8 +280,16 @@ def conformance(
         candidate_distances[denominator < self.mahal_distance.zero_thres] = 0
         candidate_distances[rho > self.mahal_distance.subspace_thres] = np.inf
 
-        # compute the minimum of the candidate distances for each data point
-        if return_indices:
-            return np.min(candidate_distances, axis=-1), train_indices
+    
+        if take_median:
+            # compute the median of the k nearest neighbour distances for each data point for robustness
+            if return_indices:
+                return np.median(candidate_distances, axis=-1), train_indices
+            return np.median(candidate_distances, axis=-1)
+        
+        else:
+            # compute the minimum of the candidate distances for each data point = 1-nearest neighbour distance (the vanilla implementation)
+            if return_indices:
+                return np.min(candidate_distances, axis=-1), train_indices
 
-        return np.min(candidate_distances, axis=-1)
+            return np.min(candidate_distances, axis=-1)
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