Correct the description of anomaly monitor tolerance (#212)

Co-authored-by: Mano Toth <[email protected]>

Author: benenharrington

monitor-data/anomaly-monitors.mdx

@@ -17,7 +17,7 @@ To create an anomaly monitor, follow these steps:
 1. Name your monitor and add a description.
 1. Configure the monitor using the following options:
     - The comparison operator is the rule to apply when comparing the results to the expected value. The possible values are **above**, **below**, and **above or below**.
-    - The tolerance factor controls the sensitivity of the monitor. It defines how much deviation from the expected value to accept without triggering the monitor. It’s a range above and below the expected value. The higher the tolerance factor, the wider this range. When the results of the aggregation stay within this range, the monitor doesn’t trigger. When the results of the aggregation cross this range, the monitor triggers. The tolerance factor can be any positive numeric value.
+    - The tolerance factor controls the sensitivity of the monitor. Axiom combines the tolerance factor with a measure of how much the results of your query tend to vary, and uses them to determine how much deviation from the expected value to tolerate before triggering the monitor. The higher the tolerance factor, the wider the tolerated range of deviation. When the results of the aggregation stay within this range, the monitor doesn’t trigger. When the results of the aggregation cross this range, the monitor triggers. The tolerance factor can be any positive numeric value.
     - The frequency is how often the monitor runs. This is a positive integer number of minutes.
     - The range is the time range for your query. This is a positive integer number of minutes. A longer time range allows the anomaly monitor to consider a larger number of datapoints when calculating the expected value.
     - **Alert on no data** triggers the monitor when your query doesn’t return any data. Your query returns no data if no events match your filters and an aggregation used in the query is undefined. For example, you take the average of a field not present in any matching events.