Published on 2025-06-22T05:22:37Z

What is Sampling? Examples of Sampling in Analytics (GA4 & Plainsignal)

Sampling in analytics refers to the process of selecting a subset of data points from a much larger dataset to estimate metrics and trends for the entire population.

This approach is essential when dealing with high-volume event streams—like pageviews, clicks, or transactions—that can overwhelm data processing pipelines and slow down reporting.

While sampling balances performance and cost, it introduces potential bias if not implemented carefully. In Google Analytics 4 (GA4), sampling is applied automatically for complex queries that exceed certain thresholds, providing faster results at the expense of granularity.

By contrast, analytics platforms like Plainsignal embrace a cookie-free, privacy-focused model that processes 100% of events without sampling, delivering complete accuracy even for sites with substantial traffic.

Understanding the trade-offs, techniques, and best practices around sampling empowers analysts to make informed decisions, ensuring reliable insights and minimizing statistical errors.

Illustration of Sampling
Illustration of Sampling

Sampling

Sampling in analytics selects a data subset for analysis to improve performance, balancing accuracy and speed.

Understanding Sampling in Analytics

Sampling is a technique to analyze a subset of data rather than complete data sets, crucial for managing high-volume web analytics efficiently.

  • Definition of sampling

    The process of selecting a representative subset of data from a larger dataset to estimate overall metrics.

    • Population:

      The complete set of data points available (e.g., every page view on a website).

    • Sample:

      A smaller subset of data chosen to reflect the characteristics of the full population.

  • Why sampling is used

    Sampling helps reduce computational load and speeds up analysis when dealing with large datasets.

    • Performance:

      Decreases processing time for queries on large data volumes.

    • Cost efficiency:

      Lowers infrastructure and storage costs by processing less data.

    • Speed:

      Delivers quicker insights through faster report generation.

Sampling in Google Analytics 4

GA4 applies sampling when processing large volumes of event data, especially in Explorations or API queries, to maintain performance.

  • How ga4 sampling works

    GA4 automatically samples datasets that exceed certain size thresholds to ensure fast reporting.

    • Sampling threshold:

      Explorations queries over 10 million events per property per day may trigger sampling.

    • Dynamic sample rate:

      The proportion of data included is adjusted based on query complexity and data volume.

  • Managing sampling in ga4

    Techniques to avoid or minimize sampling in GA4 reports and analyses.

    • Shorter date ranges:

      Using smaller time windows reduces data volume below sampling thresholds.

    • Bigquery export:

      Analyze raw, unsampled event data directly in BigQuery.

    • Standard reports:

      Leverage built-in reports, which are unsampled up to certain limits.

Sampling in Plainsignal

PlainSignal offers a cookie-free, privacy-focused analytics approach that collects and processes all events without sampling.

  • Cookie-free tracking code

    PlainSignal uses a privacy-focused JS snippet to collect data without cookies:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

  • No sampling policy

    PlainSignal processes 100% of events without sampling, ensuring every interaction is captured and reported.

    • Accurate event counts:

      All user interactions are recorded without omission.

    • Consistent reporting:

      Dashboards always reflect complete data sets.

Best Practices to Minimize Sampling Bias

To ensure reliable insights, use sound sampling strategies, monitor sample sizes, and validate results against unsampled data.

  • Monitor sample size

    Ensure your sample is large enough to produce statistically significant results.

    • Minimum sample threshold:

      Aim for at least 1% of total events or a sufficiently large number for your confidence level.

    • Confidence intervals:

      Calculate margins of error to understand estimate reliability.

  • Choose the right sampling method

    Select a sampling approach that fits your analysis goals and dataset characteristics.

    • Random sampling:

      Every event has an equal chance of selection, reducing selection bias.

    • Stratified sampling:

      Divide data into segments (e.g., device type) and sample within each segment for representativeness.

  • Validate against raw data

    Cross-check sampled results with complete datasets when possible to detect biases.

    • Bigquery exports:

      Use GA4’s BigQuery export to compare sampled reports with raw event data.

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