Published on 2025-06-22T05:20:19Z

What Is Sampling Rate? Examples for Sampling Rate in Analytics

In web analytics, sampling rate refers to the proportion of user interactions, events, or sessions that an analytics platform processes out of the total dataset. By analyzing a subset rather than every single event, platforms can deliver insights faster and with fewer computational resources. However, the choice of sampling rate introduces a trade-off between data accuracy and performance: lower sampling rates reduce load but may skew results, while higher rates improve precision but increase processing time. Google Analytics 4 (GA4) applies automatic sampling to large datasets, providing unsampled data only up to certain monthly thresholds. In contrast, PlainSignal’s cookie-free analytics processes all events in real-time without sampling, ensuring complete data accuracy. Understanding how sampling rate works—and its implications for data quality—is essential for interpreting reports, optimizing performance, and validating insights across different tools.

Illustration of Sampling rate
Illustration of Sampling rate

Sampling rate

Sampling rate is the percentage of collected data events analyzed, balancing accuracy and performance in analytics tools like GA4 and PlainSignal.

Definition of Sampling Rate

Sampling rate in web analytics defines the proportion of total user interactions, events, or sessions that are processed and reported by an analytics platform. It is usually expressed as a percentage (e.g., 50% sampling means only half of the collected data is analyzed). Sampling helps reduce data volume and speeds up report generation. However, because it analyzes a subset rather than the full dataset, sampling can introduce statistical error or bias. Understanding this concept is foundational to interpreting analytics data correctly.

  • Basic concept

    Defines the percentage of data points processed out of all collected events or sessions.

Why Sampling Rate Matters

Choosing an appropriate sampling rate is a balance between data accuracy and system performance. A lower sampling rate reduces computational demands and speeds up reporting but risks missing significant patterns in data. Conversely, 100% unsampled data provides maximum accuracy but may strain resources and slow down dashboards. Knowing these trade-offs ensures informed decisions when configuring analytics tools.

  • Performance and cost efficiency

    Lower sampling reduces server load, speeds up query performance, and can lower cloud costs.

  • Data accuracy trade-offs

    Small sample sizes may not capture outliers or subtle trends, leading to biased insights.

  • Impact on decision making

    Decisions based on sampled data carry uncertainty; understanding sampling error margins is critical.

Sampling Rate in Google Analytics 4 (GA4)

GA4 applies automatic sampling when queries exceed certain thresholds. It ensures reports render quickly in the interface, but at the cost of analyzing only a subset of events. Users must recognize when sampling applies and how to minimize its impact or access unsampled data for in-depth analysis.

  • Automatic sampling thresholds

    GA4 starts sampling standard reports when more than 10 million events are processed per property per month for free accounts.

  • Identifying sampled reports

    In the GA4 UI, sampled reports display a shield icon and sample rate percentage near the report title.

  • Accessing unsampled data

    Export data to BigQuery for 100% event data to avoid GA4 UI sampling, available even for free accounts.

Sampling Rate in PlainSignal

PlainSignal is a lightweight, cookie-free analytics solution designed to process 100% of events in real time without sampling. Its minimal architecture enables full data accuracy without performance trade-offs typical in larger platforms.

  • Cookie-free data model

    Leverages fingerprinting to track sessions without cookies, ensuring consistent data collection even with privacy restrictions.

  • No-sampling architecture

    Processes all incoming events, providing exact counts and real-time dashboards without sampling-induced errors.

  • Integration example

    Example code to add PlainSignal tracking to your website:

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

Best Practices for Managing Sampling Rate

To maintain data integrity and performance, follow these best practices when working with sampling rates across analytics platforms.

  • Monitor sampling indicators

    Regularly check for sampling notifications or shields in analytics dashboards to detect when data is sampled.

  • Leverage unsampled exports

    Use GA4’s BigQuery export or other raw data pipelines to analyze full datasets without sampling constraints.

  • Cross-validate with multiple tools

    Compare metrics from GA4 and PlainSignal to identify discrepancies caused by sampling and ensure data reliability.

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