[DOCS] Clarifies terminology in Performing population analysis page. (#74237)

Author: szabosteve

docs/reference/ml/anomaly-detection/ml-configuring-populations.asciidoc

@@ -7,18 +7,18 @@ Entities or events in your data can be considered anomalous when:
 * Their behavior changes over time, relative to their own previous behavior, or
 * Their behavior is different than other entities in a specified population.
 
-The latter method of detecting outliers is known as _population analysis_. The
-{ml} analytics build a profile of what a "typical" user, machine, or other entity
-does over a specified time period and then identify when one is behaving
+The latter method of detecting anomalies is known as _population analysis_. The
+{ml} analytics build a profile of what a "typical" user, machine, or other 
+entity does over a specified time period and then identify when one is behaving
 abnormally compared to the population.
 
 This type of analysis is most useful when the behavior of the population as a
-whole is mostly homogeneous and you want to identify outliers. In general,
-population analysis is not useful when members of the population inherently
-have vastly different behavior. You can, however, segment your data into groups
-that behave similarly and run these as separate jobs. For example, you can use a
-query filter in the {dfeed} to segment your data or you can use the
-`partition_field_name` to split the analysis for the different groups.
+whole is mostly homogeneous and you want to identify unusual behavior. In 
+general, population analysis is not useful when members of the population 
+inherently have vastly different behavior. You can, however, segment your data 
+into groups that behave similarly and run these as separate jobs. For example, 
+you can use a query filter in the {dfeed} to segment your data or you can use 
+the `partition_field_name` to split the analysis for the different groups.
 
 Population analysis scales well and has a lower resource footprint than
 individual analysis of each series. For example, you can analyze populations
@@ -52,8 +52,8 @@ PUT _ml/anomaly_detectors/population
 ----------------------------------
 // TEST[skip:needs-licence]
 
-<1> This `over_field_name` property indicates that the metrics for each client (
-  as identified by their IP address) are analyzed relative to other clients
+<1> This `over_field_name` property indicates that the metrics for each client 
+  (as identified by their IP address) are analyzed relative to other clients
   in each bucket.
 
 If your data is stored in {es}, you can use the population job wizard in {kib}
@@ -73,8 +73,8 @@ image::images/ml-population-results.png["Population analysis results in the Anom
 
 As in this case, the results are often quite sparse. There might be just a few
 data points for the selected time period. Population analysis is particularly
-useful when you have many entities and the data for specific entitles is sporadic
-or sparse.
+useful when you have many entities and the data for specific entitles is 
+sporadic or sparse.
 
 If you click on a section in the timeline or swim lanes, you can see more
 details about the anomalies: