diff --git a/src/pytorch_ie/metrics/statistics.py b/src/pytorch_ie/metrics/statistics.py
index a3cf86d9..53c869d9 100644
--- a/src/pytorch_ie/metrics/statistics.py
+++ b/src/pytorch_ie/metrics/statistics.py
@@ -1,3 +1,4 @@
+import logging
 from collections import defaultdict
 from typing import Any, Dict, List, Optional, Type, Union
 
@@ -6,6 +7,8 @@
 from pytorch_ie.core import Document, DocumentStatistic
 from pytorch_ie.documents import TextBasedDocument
 
+logger = logging.getLogger(__name__)
+
 
 class TokenCountCollector(DocumentStatistic):
     """Collects the token count of a field when tokenizing its content with a Huggingface tokenizer.
@@ -120,6 +123,18 @@ def __init__(
         self.label_attribute = label_attribute
         if not (isinstance(labels, list) or labels == "INFERRED"):
             raise ValueError("labels must be a list of strings or 'INFERRED'")
+        if labels == "INFERRED":
+            logger.warning(
+                f"Inferring labels with {self.__class__.__name__} from data produces wrong results "
+                f"for certain aggregation functions (e.g. 'mean', 'std', 'min') because zero values "
+                f"are not included in the calculation. We remove these aggregation functions from "
+                f"this collector, but be aware that the results may be wrong for your own aggregation "
+                f"functions that rely on zero values."
+            )
+            self.aggregation_functions = [
+                func for func in self.aggregation_functions if func not in ["mean", "std", "min"]
+            ]
+
         self.labels = labels
 
     def _collect(self, doc: Document) -> Dict[str, int]: