diff --git a/docs/statsig-warehouse-native/metrics/normalized-metrics.md b/docs/statsig-warehouse-native/metrics/normalized-metrics.md
index e3145fa2e..97e68c5a6 100644
--- a/docs/statsig-warehouse-native/metrics/normalized-metrics.md
+++ b/docs/statsig-warehouse-native/metrics/normalized-metrics.md
@@ -12,7 +12,7 @@ With normal A/B tests the unit of randomization (e.g. UserID) matches the unit o
 For example - you've added image support to a collaborative commenting feature in your product and want to A/B test it before rollout. You randomize it using businessID. You cannot randomize by userID, since you need everyone within a single business to either have this new feature or not. If you simply compared # of comments per businessID, this data would be skewed by large companies. A business with 1000 employees, but 10 comments would "contribute more" than a business with 5 employees who made 5 comments. Normalizing a metric in this case - is normalizing by users exposed to the experiment. In this instance if 1000 and 5 users were exposed from each business, the first business would have a comments/user rate of 0.01, while the second company would have a comments/user rate of 1. This is reasonable now to compare across companies of many different sizes. 
 
  ## What it does
-Under the covers, normalizing a metric simple creates a ratio metric. The numerator is metric you're normalizing. The denominator is a COUNT DISTINCT of the UnitID you're normalizing to.
+Under the covers, normalizing a metric simply creates a ratio metric. The numerator is metric you're normalizing. The denominator is a COUNT DISTINCT of the UnitID you're normalizing to.
 If you wanted to, you could also create this ratio metric yourself and use it in experiments - this is documented [here](https://docs.statsig.com/metrics/different-id). 
 
 ## How to do it