Add disclaimer to statistical functions (#183)

Co-authored-by: Mark Ramotowski <[email protected]>

Author: tothmano

apl/aggregation-function/dcount.mdx

@@ -7,6 +7,10 @@ The `dcount` aggregation function in Axiom Processing Language (APL) counts the
 
 Use `dcount` for analyzing datasets where it’s important to identify the number of distinct occurrences, such as unique IP addresses in security logs, unique user IDs in application logs, or unique trace IDs in OpenTelemetry traces.
 
+<Note>
+The `dcount` aggregation in APL is a statistical aggregation that returns estimated results. The estimation comes with the benefit of speed at the expense of accuracy. This means that `dcount` is fast and light on resources even on a large or high-cardinality dataset, but it doesn’t provide precise results.
+</Note>
+
 ## For users of other query languages
 
 If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

apl/aggregation-function/histogram.mdx

@@ -7,6 +7,10 @@ The `histogram` aggregation in APL allows you to create a histogram that groups
 
 The `histogram` aggregation is ideal for identifying peaks, valleys, and outliers in your data. For example, you can analyze the distribution of request durations in web server logs or span durations in OpenTelemetry traces to understand performance bottlenecks.
 
+<Note>
+The `histogram` aggregation in APL is a statistical aggregation that returns estimated results. The estimation comes with the benefit of speed at the expense of accuracy. This means that `histogram` is fast and light on resources even on a large or high-cardinality dataset, but it doesn’t provide precise results.
+</Note>
+
 ## For users of other query languages
 
 If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

apl/aggregation-function/percentile.mdx

@@ -7,6 +7,10 @@ The `percentile` aggregation function in Axiom Processing Language (APL) allows
 
 You can apply the `percentile` function to various use cases, such as analyzing log data for request durations, OpenTelemetry traces for service latencies, or security logs to assess risk patterns.
 
+<Note>
+The `percentile` aggregation in APL is a statistical aggregation that returns estimated results. The estimation comes with the benefit of speed at the expense of accuracy. This means that `percentile` is fast and light on resources even on a large or high-cardinality dataset, but it doesn’t provide precise results.
+</Note>
+
 ## For users of other query languages
 
 If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

apl/aggregation-function/topk.mdx

@@ -8,7 +8,7 @@ The `topk` aggregation in Axiom Processing Language (APL) allows you to identify
 Use `topk` to find the most common or relevant entries in datasets, especially in log analysis, telemetry data, and monitoring systems. This aggregation helps you focus on the most important data points, filtering out the noise.
 
 <Note>
-The `topk` aggregation in APL is estimated. The estimation comes with the benefit of speed at the expense of accuracy. This means that `topk` is fast and light on resources even on a large or high-cardinality dataset, but it doesn’t provide the most accurate results.
+The `topk` aggregation in APL is a statistical aggregation that returns estimated results. The estimation comes with the benefit of speed at the expense of accuracy. This means that `topk` is fast and light on resources even on a large or high-cardinality dataset, but it doesn’t provide precise results.
 
 For completely accurate results, use the [`top` operator](/apl/tabular-operators/top-operator).
 </Note>