Win probability is calculated against control, not all variants

contents/docs/experiments/funnels-statistics.mdx

@@ -26,7 +26,7 @@ One more thing worth noting: Bayesian inference starts with an initial guess tha
 
 ## Win probabilities
 
-The **win probability** tells you how likely it is that a given variant has the highest conversion rate compared to all other variants. It helps you determine whether the metric shows a **statistically significant** real effect vs. simply random chance.
+The **win probability** tells you how likely it is that a given variant produces a higher conversion rate compared to the control. It helps you determine whether the metric shows a **statistically significant** real effect vs. simply random chance.
 
 Let's say you're testing a new way of presenting pineapple on the website and have these results:
 

contents/docs/experiments/statistics.mdx

@@ -23,7 +23,7 @@ Say you started an experiment a few hours ago and see these results:
 
 The first two values are pure math: dividing the number of successes by the total number of users gives us the raw success rates. It's not enough to just compare these conversion rates, however.
 
-The last two values are derived using Bayesian statistics and describe our confidence in the results. The **win probability** tells you how likely it is that a given variant has the highest conversion rate compared to all other variants in the experiment. The **credible interval** tells you the range where the true conversion rate lies with 95% probability. It is displayed _relative to the control conversion rate_, which makes it easier to understand the likelihood of a variant being better than the control (e.g. the test variant performs somewhere between 69.89% worse and 158.88% better than the control).
+The last two values are derived using Bayesian statistics and describe our confidence in the results. The **win probability** tells you how likely it is that a given variant produces a higher conversion rate compared to the control. The **credible interval** tells you the range where the true conversion rate lies with 95% probability. It is displayed _relative to the control conversion rate_, which makes it easier to understand the likelihood of a variant being better than the control (e.g. the test variant performs somewhere between 69.89% worse and 158.88% better than the control).
 
 <ProductScreenshot
   imageLight = "https://res.cloudinary.com/dmukukwp6/image/upload/wide_credible_interval_light_v2_fa81c3a2ee.png"

contents/docs/experiments/trends-continuous-statistics.mdx

@@ -36,7 +36,7 @@ One more thing worth noting: Bayesian inference starts with an initial guess tha
 
 ## Win probabilities
 
-The **win probability** tells you how likely it is that a given variant has the highest value compared to all other variants. It helps you determine whether the metric shows a **statistically significant** real effect vs. simply random chance.
+The **win probability** tells you how likely it is that a given variant produces a higher value compared to the control. It helps you determine whether the metric shows a **statistically significant** real effect vs. simply random chance.
 
 Let's say you're testing a new pricing page and have these results:
 

contents/docs/experiments/trends-count-statistics.mdx

@@ -25,7 +25,7 @@ One more thing worth noting: Bayesian inference starts with an initial guess tha
 
 ## Win probabilities
 
-The **win probability** tells you how likely it is that a given variant has the highest rate compared to all other variants. It helps you determine whether the metric shows a **statistically significant** real effect vs. simply random chance.
+The **win probability** tells you how likely it is that a given variant produces a higher rate compared to the control. It helps you determine whether the metric shows a **statistically significant** real effect vs. simply random chance.
 
 Let's say you're testing a new menu design and have these results: