diff --git a/src/lerobot/processor/normalize_processor.py b/src/lerobot/processor/normalize_processor.py
index 18a71a431..9bdec9cb7 100644
--- a/src/lerobot/processor/normalize_processor.py
+++ b/src/lerobot/processor/normalize_processor.py
@@ -322,7 +322,14 @@ class _NormalizationMixin:
 
         stats = self._tensor_stats[key]
 
-        if norm_mode == NormalizationMode.MEAN_STD and "mean" in stats and "std" in stats:
+        if norm_mode == NormalizationMode.MEAN_STD:
+            mean = stats.get("mean", None)
+            std = stats.get("std", None)
+            if mean is None or std is None:
+                raise ValueError(
+                    "MEAN_STD normalization mode requires mean and std stats, please update the dataset with the correct stats"
+                )
+
             mean, std = stats["mean"], stats["std"]
             # Avoid division by zero by adding a small epsilon.
             denom = std + self.eps
@@ -330,7 +337,14 @@ class _NormalizationMixin:
                 return tensor * std + mean
             return (tensor - mean) / denom
 
-        if norm_mode == NormalizationMode.MIN_MAX and "min" in stats and "max" in stats:
+        if norm_mode == NormalizationMode.MIN_MAX:
+            min_val = stats.get("min", None)
+            max_val = stats.get("max", None)
+            if min_val is None or max_val is None:
+                raise ValueError(
+                    "MIN_MAX normalization mode requires min and max stats, please update the dataset with the correct stats"
+                )
+
             min_val, max_val = stats["min"], stats["max"]
             denom = max_val - min_val
             # When min_val == max_val, substitute the denominator with a small epsilon
@@ -345,8 +359,14 @@ class _NormalizationMixin:
             # Map from [min, max] to [-1, 1]
             return 2 * (tensor - min_val) / denom - 1
 
-        if norm_mode == NormalizationMode.QUANTILES and "q01" in stats and "q99" in stats:
-            q01, q99 = stats["q01"], stats["q99"]
+        if norm_mode == NormalizationMode.QUANTILES:
+            q01 = stats.get("q01", None)
+            q99 = stats.get("q99", None)
+            if q01 is None or q99 is None:
+                raise ValueError(
+                    "QUANTILES normalization mode requires q01 and q99 stats, please update the dataset with the correct stats"
+                )
+
             denom = q99 - q01
             # Avoid division by zero by adding epsilon when quantiles are identical
             denom = torch.where(
@@ -356,8 +376,14 @@ class _NormalizationMixin:
                 return tensor * denom + q01
             return (tensor - q01) / denom
 
-        if norm_mode == NormalizationMode.QUANTILE10 and "q10" in stats and "q90" in stats:
-            q10, q90 = stats["q10"], stats["q90"]
+        if norm_mode == NormalizationMode.QUANTILE10:
+            q10 = stats.get("q10", None)
+            q90 = stats.get("q90", None)
+            if q10 is None or q90 is None:
+                raise ValueError(
+                    "QUANTILE10 normalization mode requires q10 and q90 stats, please update the dataset with the correct stats"
+                )
+
             denom = q90 - q10
             # Avoid division by zero by adding epsilon when quantiles are identical
             denom = torch.where(