diff --git a/website/blog/2024-11-27-test-smarter-part-2.md b/website/blog/2024-11-27-test-smarter-part-2.md
index 2055b1db3af..a721087f276 100644
--- a/website/blog/2024-11-27-test-smarter-part-2.md
+++ b/website/blog/2024-11-27-test-smarter-part-2.md
@@ -76,7 +76,7 @@ In your intermediate layer, focus on data hygiene and anomaly tests for new colu
     - Add simple anomaly tests to verify the behavior of your sets of joins and aggregations. This may look like:
         - An [accepted_values](/reference/resource-properties/data-tests#accepted_values) test on a newly calculated categorical column.
         - A [mutually_exclusive_ranges](https://github.com/dbt-labs/dbt-utils#mutually_exclusive_ranges-source) test on two columns whose values behave in relation to one another (ex: asserting age ranges do not overlap).
-        - A [not_constant](https://docs.getdbt.com/reference/resource-properties/data-tests#accepted_values) test on a column whose value should be continually changing (ex: page view counts on website analytics).
+        - A [not_constant](https://github.com/dbt-labs/dbt-utils#not_constant-source) test on a column whose value should be continually changing (ex: page view counts on website analytics).
 - Intermediate models may isolate complex operations.
     - The anomaly tests we list above may suffice here.
     - You might also consider [unit testing](https://docs.getdbt.com/docs/build/unit-tests) any particularly complex pieces of SQL logic.