Update sampling.asciidoc (#4101)

provide more accurate description on how metrics are calculated when sampling

(cherry picked from commit 485c0cd)

docs/en/observability/apm/sampling.asciidoc

@@ -105,10 +105,20 @@ A sampled trace retains all data associated with it.
 A non-sampled trace drops all <<apm-data-model-spans,span>> and <<apm-data-model-transactions,transaction>> data^1^.
 Regardless of the sampling decision, all traces retain <<apm-data-model-errors,error>> data.
 
-Some visualizations in the {apm-app}, like latency, are powered by aggregated transaction and span <<apm-data-model-metrics,metrics>>.
-Metrics are based on sampled traces and weighted by the inverse sampling rate.
-For example, if you sample at 5%, each trace is counted as 20.
-As a result, as the variance of latency increases, or the sampling rate decreases, your level of error will increase.
+Some visualizations in the {apm-app}, like latency, are powered by aggregated transaction and span <<apm-data-model-metrics,metrics>>. 
+The way these metrics are calculated depends on the sampling method used:
+
+* **Head-based sampling**: Metrics are calculated based on all sampled events.
+
+* **Tail-based sampling**: Metrics are calculated based on all events, regardless of whether they are ultimately sampled or not.
+
+* **Both head and tail-based sampling**: When both methods are used together, metrics are calculated based on all events that were sampled by the head-based sampling policy.
+
+For all sampling methods, metrics are weighted by the inverse sampling rate of the head-based sampling policy to provide an estimate of the total population. 
+For example, if your head-based sampling rate is 5%, each sampled trace is counted as 20. 
+As the variance of latency increases or the head-based sampling rate decreases, the level of error in these calculations may increase.
+
+These calculation methods ensure that the APM app provides the most accurate metrics possible given the sampling strategy in use, while also accounting for the head-based sampling rate to estimate the full population of traces.
 
 ^1^ Real User Monitoring (RUM) traces are an exception to this rule.
 The {kib} apps that utilize RUM data depend on transaction events,