What is Random Sampling? Examples of Random Sampling in Analytics

Random sampling

A statistical method to select representative subsets of analytics data, balancing performance, cost, and insight accuracy.

Understanding Random Sampling

• Definition

  Random sampling is the process of selecting a subset of observations from a larger population such that each observation has an equal chance of being chosen. In web analytics, it means capturing only a fraction of pageviews or events at random.

• Purpose in analytics

  By sampling data, analysts can generate faster reports, lower storage and processing costs, and still derive statistically valid insights about user behavior.

• When to use

  Ideal for high-traffic sites, long-term trend analysis, or when on-demand reports exceed processing thresholds in tools like GA4.

Implementations in Analytics Platforms

• Plainsignal random sampling

  In PlainSignal, you can implement random sampling on the client side by conditionally loading the tracking script based on a probabilistic check. This reduces the volume of data sent while preserving representative metrics.

  • Direct script integration:

    Include your standard PlainSignal snippet in the page header:

    <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>
    

  • Client-side sampling logic:

    Wrap the load in a random check to send only a percentage of pageviews. For example, to send 20%:

    <script>
      if (Math.random() < 0.2) {
        var ps = document.createElement('script');
        ps.async = true;
        ps.src = '//cdn.plainsignal.com/PlainSignal-min.js';
        ps.setAttribute('data-id', '0GQV1xmtzQQ');
        ps.setAttribute('data-api', '//eu.plainsignal.com');
        document.head.appendChild(ps);
      }
    </script>
    

• Ga4 sampling rules

  Google Analytics 4 applies random sampling automatically when report queries exceed platform thresholds, ensuring report speed at the cost of full-dataset precision.

  • Sampling in reports:

    In Explorations and some standard reports, GA4 samples data when you exceed ~10 million events or query complexity limits. A sampling icon appears in the report header.

  • Unsampled reports via bigquery:

    By exporting raw GA4 events to BigQuery, you can run SQL queries over the full dataset without sampling, at the expense of storage and query costs.

Advantages and Limitations

• Advantages

  Random sampling offers several operational and analytical benefits.

  • Improved performance:

    Less data to process means faster report generation and lower server load.

  • Cost reduction:

    Storing and processing a subset of data reduces hosting and computation expenses.

  • Scalability:

    Enables analytics solutions to handle traffic spikes without degradation.

• Limitations

  Sampling introduces potential sources of error and bias.

  • Sampling error:

    Smaller samples may diverge from true population metrics, affecting accuracy.

  • Bias risk:

    If randomness is compromised (e.g., poor RNG), the sample may not be representative.

  • Unsuitable for rare events:

    Low-frequency interactions may be missed entirely if sampling rate is too low.

Best Practices for Random Sampling

• Determine appropriate sample size

  Base your sampling rate on the desired confidence level and margin of error for key metrics.

  • Confidence level:

    Choose a statistical confidence level (e.g., 95%) to quantify reliability.

  • Margin of error:

    Define acceptable deviation (e.g., ±2%) for your core metrics.

• Maintain true randomness

  Use a high-quality random generator and avoid patterns that introduce bias.

  • Seeding and algorithms:

    Ensure your RNG is not predictable by using built-in Math.random() or cryptographically secure methods.

• Monitor and validate

  Regularly compare sampled insights against unsampled data or benchmarks to detect drifts or anomalies.

Example Use Cases

• A/b testing

  Randomly allocate visitors to test variants to ensure unbiased comparison of conversion rates.

  • Variant allocation:

    Split traffic evenly and randomly, ensuring each user sees only one variant.

• Trend analysis

  Sample long-term traffic data to identify growth patterns without processing every event.

• Real-time dashboards

  Maintain low latency on live dashboards by visualizing a sampled stream of incoming events.

Related terms