Use consistent constant names

posthog/hogql_queries/experiments/trends_statistics_v2_count.py

@@ -7,9 +7,8 @@
 import numpy as np
 
 # Prior parameters (minimal prior knowledge)
-PRIOR_ALPHA = 1
-PRIOR_BETA = 1
-
+ALPHA_0 = 1
+BETA_0 = 1
 SAMPLE_SIZE = 10000
 
 
@@ -56,17 +55,17 @@ def calculate_probabilities_v2_count(
         raise ValidationError("Can't calculate experiment results for less than 2 variants", code="no_data")
 
     # Calculate posterior parameters for control
-    alpha_control = PRIOR_ALPHA + control_variant.count
-    beta_control = PRIOR_BETA + control_variant.absolute_exposure
+    alpha_control = ALPHA_0 + control_variant.count
+    beta_control = BETA_0 + control_variant.absolute_exposure
 
     # Draw samples from control posterior
     samples_control = gamma.rvs(alpha_control, scale=1 / beta_control, size=SAMPLE_SIZE)
 
     # Draw samples for each test variant
     test_samples = []
     for test in test_variants:
-        alpha_test = PRIOR_ALPHA + test.count
-        beta_test = PRIOR_BETA + test.absolute_exposure
+        alpha_test = ALPHA_0 + test.count
+        beta_test = BETA_0 + test.absolute_exposure
         test_samples.append(gamma.rvs(alpha_test, scale=1 / beta_test, size=SAMPLE_SIZE))
 
     # Calculate probabilities
@@ -187,8 +186,8 @@ def calculate_credible_intervals_v2_count(variants, lower_bound=0.025, upper_bou
     for variant in variants:
         try:
             # Calculate posterior parameters using absolute_exposure
-            alpha_posterior = PRIOR_ALPHA + variant.count
-            beta_posterior = PRIOR_BETA + variant.absolute_exposure
+            alpha_posterior = ALPHA_0 + variant.count
+            beta_posterior = BETA_0 + variant.absolute_exposure
 
             # Calculate credible intervals using the posterior distribution
             credible_interval = gamma.ppf([lower_bound, upper_bound], alpha_posterior, scale=1 / beta_posterior)