diff --git a/snuba/web/rpc/common/aggregation.py b/snuba/web/rpc/common/aggregation.py
index 4045e8880c..96ea4818d0 100644
--- a/snuba/web/rpc/common/aggregation.py
+++ b/snuba/web/rpc/common/aggregation.py
@@ -21,6 +21,7 @@
 from snuba.web.rpc.common.exceptions import BadSnubaRPCRequestException
 
 sampling_weight_column = column("sampling_weight")
+sign_column = column("sign")
 
 # Z value for 95% confidence interval is 1.96 which comes from the normal distribution z score.
 z_value = 1.96
@@ -211,26 +212,27 @@ def get_extrapolated_function(
         Function.ValueType, CurriedFunctionCall | FunctionCall
     ] = {
         Function.FUNCTION_SUM: f.sum(
-            f.multiply(field, sampling_weight_column), **alias_dict
+            f.multiply(field, f.multiply(sign_column, sampling_weight_column)),
+            **alias_dict,
         ),
         Function.FUNCTION_AVERAGE: f.divide(
-            f.sum(f.multiply(field, sampling_weight_column)),
+            f.sum(f.multiply(field, f.multiply(sign_column, sampling_weight_column))),
             f.sumIf(
-                sampling_weight_column,
+                f.multiply(sign_column, sampling_weight_column),
                 get_field_existence_expression(aggregation),
             ),
             **alias_dict,
         ),
         Function.FUNCTION_AVG: f.divide(
-            f.sum(f.multiply(field, sampling_weight_column)),
+            f.sum(f.multiply(field, f.multiply(sign_column, sampling_weight_column))),
             f.sumIf(
-                sampling_weight_column,
+                f.multiply(sign_column, sampling_weight_column),
                 get_field_existence_expression(aggregation),
             ),
             **alias_dict,
         ),
         Function.FUNCTION_COUNT: f.sumIf(
-            sampling_weight_column,
+            f.multiply(sign_column, sampling_weight_column),
             get_field_existence_expression(aggregation),
             **alias_dict,
         ),
@@ -407,9 +409,17 @@ def aggregation_to_expression(aggregation: AttributeAggregation) -> Expression:
     alias = aggregation.label if aggregation.label else None
     alias_dict = {"alias": alias} if alias else {}
     function_map: dict[Function.ValueType, CurriedFunctionCall | FunctionCall] = {
-        Function.FUNCTION_SUM: f.sum(field, **alias_dict),
-        Function.FUNCTION_AVERAGE: f.avg(field, **alias_dict),
-        Function.FUNCTION_COUNT: f.count(field, **alias_dict),
+        Function.FUNCTION_SUM: f.sum(f.multiply(field, sign_column), **alias_dict),
+        Function.FUNCTION_AVERAGE: f.divide(
+            f.sum(f.multiply(field, sign_column)),
+            f.sumIf(sign_column, get_field_existence_expression(aggregation)),
+            **alias_dict,
+        ),
+        Function.FUNCTION_COUNT: f.sumIf(
+            sign_column,
+            get_field_existence_expression(aggregation),
+            **alias_dict,
+        ),
         Function.FUNCTION_P50: cf.quantile(0.5)(field, **alias_dict),
         Function.FUNCTION_P75: cf.quantile(0.75)(field, **alias_dict),
         Function.FUNCTION_P90: cf.quantile(0.9)(field, **alias_dict),