Incorporate continuous methods into TrendsQueryRunner

PostHog · Dec 19, 2024 · 54f989e · 54f989e
1 parent 78af92d
commit 54f989e
Showing 1 changed file with 17 additions and 3 deletions.
diff --git a/posthog/hogql_queries/experiments/experiment_trends_query_runner.py b/posthog/hogql_queries/experiments/experiment_trends_query_runner.py
@@ -14,6 +14,11 @@
     calculate_credible_intervals_v2_count,
     calculate_probabilities_v2_count,
 )
+from posthog.hogql_queries.experiments.trends_statistics_v2_continuous import (
+    are_results_significant_v2_continuous,
+    calculate_credible_intervals_v2_continuous,
+    calculate_probabilities_v2_continuous,
+)
 from posthog.hogql_queries.insights.trends.trends_query_runner import TrendsQueryRunner
 from posthog.hogql_queries.query_runner import QueryRunner
 from posthog.models.experiment import Experiment
@@ -316,9 +321,18 @@ def run(query_runner: TrendsQueryRunner, result_key: str, is_parallel: bool):
         # Statistical analysis
         control_variant, test_variants = self._get_variants_with_base_stats(count_result, exposure_result)
         if self.stats_version == 2:
-            probabilities = calculate_probabilities_v2_count(control_variant, test_variants)
-            significance_code, p_value = are_results_significant_v2_count(control_variant, test_variants, probabilities)
-            credible_intervals = calculate_credible_intervals_v2_count([control_variant, *test_variants])
+            if self.query.count_query.series[0].math:
+                probabilities = calculate_probabilities_v2_continuous(control_variant, test_variants)
+                significance_code, p_value = are_results_significant_v2_continuous(
+                    control_variant, test_variants, probabilities
+                )
+                credible_intervals = calculate_credible_intervals_v2_continuous([control_variant, *test_variants])
+            else:
+                probabilities = calculate_probabilities_v2_count(control_variant, test_variants)
+                significance_code, p_value = are_results_significant_v2_count(
+                    control_variant, test_variants, probabilities
+                )
+                credible_intervals = calculate_credible_intervals_v2_count([control_variant, *test_variants])
         else:
             probabilities = calculate_probabilities(control_variant, test_variants)
             significance_code, p_value = are_results_significant(control_variant, test_variants, probabilities)