diff --git a/src/iceberg/expression/strict_metrics_evaluator.cc b/src/iceberg/expression/strict_metrics_evaluator.cc
index e2fe34f14..a9b5ded46 100644
--- a/src/iceberg/expression/strict_metrics_evaluator.cc
+++ b/src/iceberg/expression/strict_metrics_evaluator.cc
@@ -142,7 +142,9 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
       return kRowsMightNotMatch;
     }
 
-    if (CanContainNulls(id) || CanContainNaNs(id)) {
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nulls, CanContainNulls(id));
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nans, CanContainNaNs(id));
+    if (has_nulls || has_nans) {
       return kRowsMightNotMatch;
     }
 
@@ -168,7 +170,9 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
       return kRowsMightNotMatch;
     }
 
-    if (CanContainNulls(id) || CanContainNaNs(id)) {
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nulls, CanContainNulls(id));
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nans, CanContainNaNs(id));
+    if (has_nulls || has_nans) {
       return kRowsMightNotMatch;
     }
 
@@ -194,7 +198,9 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
       return kRowsMightNotMatch;
     }
 
-    if (CanContainNulls(id) || CanContainNaNs(id)) {
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nulls, CanContainNulls(id));
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nans, CanContainNaNs(id));
+    if (has_nulls || has_nans) {
       return kRowsMightNotMatch;
     }
 
@@ -226,7 +232,9 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
       return kRowsMightNotMatch;
     }
 
-    if (CanContainNulls(id) || CanContainNaNs(id)) {
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nulls, CanContainNulls(id));
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nans, CanContainNaNs(id));
+    if (has_nulls || has_nans) {
       return kRowsMightNotMatch;
     }
 
@@ -258,7 +266,9 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
       return kRowsMightNotMatch;
     }
 
-    if (CanContainNulls(id) || CanContainNaNs(id)) {
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nulls, CanContainNulls(id));
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nans, CanContainNaNs(id));
+    if (has_nulls || has_nans) {
       return kRowsMightNotMatch;
     }
     auto lower_it = data_file_.lower_bounds.find(id);
@@ -330,7 +340,9 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
       return kRowsMightNotMatch;
     }
 
-    if (CanContainNulls(id) || CanContainNaNs(id)) {
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nulls, CanContainNulls(id));
+    ICEBERG_ASSIGN_OR_RAISE(auto has_nans, CanContainNaNs(id));
+    if (has_nulls || has_nans) {
       return kRowsMightNotMatch;
     }
     auto lower_it = data_file_.lower_bounds.find(id);
@@ -435,19 +447,43 @@ class StrictMetricsVisitor : public BoundVisitor<bool> {
     return Literal::Deserialize(stats, primitive_type);
   }
 
-  bool CanContainNulls(int32_t id) {
+  Result<bool> CanContainNulls(int32_t id) {
+    ICEBERG_ASSIGN_OR_RAISE(auto field_opt, schema_.FindFieldById(id));
+    if (field_opt.has_value() && !field_opt->get().optional()) {
+      return false;
+    }
+
+    // When null_value_counts is not empty, the evaluator expects all fields to be
+    // present. A missing entry indicates that the field's null count is unknown.
     if (data_file_.null_value_counts.empty()) {
       return true;
     }
     auto it = data_file_.null_value_counts.find(id);
-    return it != data_file_.null_value_counts.cend() && it->second > 0;
+    if (it == data_file_.null_value_counts.cend()) {
+      return true;
+    }
+    return it->second > 0;
   }
 
-  bool CanContainNaNs(int32_t id) {
+  Result<bool> CanContainNaNs(int32_t id) {
+    ICEBERG_ASSIGN_OR_RAISE(auto field_opt, schema_.FindFieldById(id));
+    if (field_opt.has_value()) {
+      auto type_id = field_opt->get().type()->type_id();
+      if (type_id != TypeId::kFloat && type_id != TypeId::kDouble) {
+        return false;
+      }
+    }
+
     // nan counts might be null for early version writers when nan counters are not
     // populated.
+    if (data_file_.nan_value_counts.empty()) {
+      return true;
+    }
     auto it = data_file_.nan_value_counts.find(id);
-    return it != data_file_.nan_value_counts.cend() && it->second > 0;
+    if (it == data_file_.nan_value_counts.cend()) {
+      return true;
+    }
+    return it->second > 0;
   }
 
   bool ContainsNullsOnly(int32_t id) {
diff --git a/src/iceberg/test/strict_metrics_evaluator_test.cc b/src/iceberg/test/strict_metrics_evaluator_test.cc
index fa6185c3b..fcb997929 100644
--- a/src/iceberg/test/strict_metrics_evaluator_test.cc
+++ b/src/iceberg/test/strict_metrics_evaluator_test.cc
@@ -846,4 +846,67 @@ TEST_F(StrictMetricsEvaluatorMigratedTest, EvaluateOnNestedColumnWithStats) {
   ExpectShouldRead(Expressions::NotNull("struct.nested_col_with_stats"), false);
 }
 
+TEST(StrictMetricsEvaluatorRegressionTest, MissingNullCountForField) {
+  // Field 14 (no_nan_stats, float64, optional) has bounds and value_counts but is
+  // missing from null_value_counts. The evaluator must conservatively assume nulls
+  // may exist and return kRowsMightNotMatch for comparison operators.
+  auto schema = std::make_shared<Schema>(
+      std::vector<SchemaField>{
+          SchemaField::MakeOptional(14, "no_nan_stats", float64()),
+      },
+      /*schema_id=*/0);
+
+  auto data_file = std::make_shared<DataFile>();
+  data_file->file_path = "null_test.parquet";
+  data_file->file_format = FileFormatType::kParquet;
+  data_file->record_count = 50;
+  data_file->value_counts = {{14, 50L}};
+  data_file->null_value_counts = {{4, 0L}, {5, 0L}};
+  data_file->nan_value_counts = {{14, 0L}};
+  data_file->lower_bounds = {{14, Literal::Double(1.0).Serialize().value()}};
+  data_file->upper_bounds = {{14, Literal::Double(100.0).Serialize().value()}};
+
+  auto evaluate = [&](const std::shared_ptr<Expression>& expr) {
+    ICEBERG_UNWRAP_OR_FAIL(auto eval, StrictMetricsEvaluator::Make(expr, schema, true));
+    ICEBERG_UNWRAP_OR_FAIL(auto result, eval->Evaluate(*data_file));
+    EXPECT_EQ(result, kRowsMightNotMatch) << expr->ToString();
+  };
+
+  evaluate(Expressions::LessThan("no_nan_stats", Literal::Double(200.0)));
+  evaluate(Expressions::LessThanOrEqual("no_nan_stats", Literal::Double(200.0)));
+  evaluate(Expressions::GreaterThan("no_nan_stats", Literal::Double(-1.0)));
+  evaluate(Expressions::GreaterThanOrEqual("no_nan_stats", Literal::Double(-1.0)));
+  evaluate(Expressions::Equal("no_nan_stats", Literal::Double(50.0)));
+}
+
+TEST(StrictMetricsEvaluatorRegressionTest, MissingNanCountForField) {
+  // Field 14 (no_nan_stats, float64, optional) is missing from nan_value_counts.
+  // For a floating-point field, the evaluator must conservatively assume NaNs may
+  // exist and return kRowsMightNotMatch for comparison operators.
+  auto schema = std::make_shared<Schema>(
+      std::vector<SchemaField>{
+          SchemaField::MakeOptional(14, "no_nan_stats", float64()),
+      },
+      /*schema_id=*/0);
+
+  auto data_file = std::make_shared<DataFile>();
+  data_file->file_path = "nan_test.parquet";
+  data_file->file_format = FileFormatType::kParquet;
+  data_file->record_count = 50;
+  data_file->value_counts = {{14, 50L}};
+  data_file->null_value_counts = {{14, 0L}};
+  data_file->nan_value_counts = {{8, 0L}};
+  data_file->lower_bounds = {{14, Literal::Double(1.0).Serialize().value()}};
+  data_file->upper_bounds = {{14, Literal::Double(100.0).Serialize().value()}};
+
+  auto evaluate = [&](const std::shared_ptr<Expression>& expr) {
+    ICEBERG_UNWRAP_OR_FAIL(auto eval, StrictMetricsEvaluator::Make(expr, schema, true));
+    ICEBERG_UNWRAP_OR_FAIL(auto result, eval->Evaluate(*data_file));
+    EXPECT_EQ(result, kRowsMightNotMatch) << expr->ToString();
+  };
+
+  evaluate(Expressions::LessThan("no_nan_stats", Literal::Double(200.0)));
+  evaluate(Expressions::GreaterThan("no_nan_stats", Literal::Double(-1.0)));
+}
+
 }  // namespace iceberg