anchor.diff:  * 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
  
positive.code: 
  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.
  
  If you click on a section in the timeline or swim lanes, you can see more
  details about the anomalies:
  
negative.code: ----------------------------------
  // 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
    in each bucket.
  
  If your data is stored in {es}, you can use the population job wizard in {kib}
  