wip

Author: shellmayr

develop-docs/application-architecture/dynamic-sampling/extrapolation.mdx

@@ -24,7 +24,7 @@ At first glance, extrapolation may seem unnecessarily complicated. However, for
 
 - **The numbers correspond to the real world**: When data is sampled, there is some math you need to do to infer what the real numbers are, e.g., when you have 1000 samples at 10% sample rate, there are 10000 requests to your application. With extrapolation, you don't have to know your sample rate to understand what your application is actually doing. Instead, you get a view of the real behavior without additional knowledge or math required on your end.
 
-- **Steady time series when sample rates change**: Whenever you change sample rates, both the count and possibly the distribution of the values will change in some way. When you switch the sample rate from 10% to 1% for whatever reason, there will be a sudden change in all associated metrics. Extrapolation corrects for this, so your graphs are steady, and your alerts don't fire when this happens.
+- **Steady timeseries when sample rates change**: Whenever you change sample rates, both the count and possibly the distribution of the values will change in some way. When you switch the sample rate from 10% to 1% for whatever reason, there will be a sudden change in all associated metrics. Extrapolation corrects for this, so your graphs are steady, and your alerts don't fire when this happens.
 - **Combining different sample rates**: When your endpoints don't have the same sample rate, how are you supposed to know the true p90 when one of your endpoints is sampled at 1% and another at 100%, but all you get is the aggregate of the samples?
 
 ## How Does Extrapolation Work?