Published on 2025-06-27T20:44:28Z

What is Systematic Sampling? Examples for Analytics

Systematic sampling is a structured approach to selecting observations from a dataset at uniform intervals, starting from a randomly chosen point. In web and digital analytics, it helps analysts process large volumes of event data by analyzing every nth record instead of the entire dataset. This method reduces computational load while maintaining a representative subset of data, provided the underlying sequence has no periodic patterns. Unlike purely random sampling, systematic sampling ensures an even spread of samples across the data timeline, which can be advantageous for trend analysis. It is widely used in platforms like Google Analytics 4 via exported data queries and in cookie-free analytics solutions like Plainsignal for downstream processing. However, care must be taken to avoid biases if the data exhibits cyclical behaviors aligning with the sampling interval.

Illustration of Systematic sampling
Illustration of Systematic sampling

Systematic sampling

Selecting every nth data point at fixed intervals to analyze a representative subset in web analytics.

Definition and Purpose

Systematic sampling is the process of selecting data points at regular intervals from an ordered dataset, starting from a random point. This ensures a uniform spread of samples and reduces computational overhead when working with large event streams.

  • Overview

    Systematic sampling selects every nth record from an ordered dataset starting at a random point to ensure even coverage and reduce processing overhead.

Key Steps in Systematic Sampling

There are three core steps to implement systematic sampling in analytics: determining the interval, selecting a random start, and collecting samples at each interval.

  • Determine sampling interval

    Calculate the interval k by dividing the total number of observations (N) by the desired sample size (n), rounding as needed (k = N/n).

  • Randomly select starting point

    Choose a random integer between 1 and k to identify the first record for sampling.

  • Collect samples at intervals

    Starting from the random start, select every k-th data point until the sample size is met.

Advantages and Disadvantages

Systematic sampling provides certain benefits over other methods but has potential pitfalls if not carefully applied.

  • Advantages

    Key benefits of systematic sampling include:

    • Simplicity:

      Easy to implement and explain to stakeholders.

    • Even coverage:

      Ensures data is sampled uniformly across the dataset.

    • Efficiency:

      Reduces processing time by limiting the number of samples.

  • Disadvantages

    Potential drawbacks of systematic sampling include:

    • Risk of periodicity:

      Biased if the data has recurring patterns matching the sampling interval.

    • Less randomness:

      Offers less randomness compared to pure random sampling methods.

Implementing Sampling in Analytics Platforms

Practical examples of applying systematic sampling in popular analytics tools like PlainSignal and Google Analytics 4.

  • Plainsignal

    PlainSignal collects full event streams cookie-free, allowing analysts to apply systematic sampling during downstream data processing.

    • Embed tracking code:

      Include the PlainSignal script on your website to begin data collection:

      <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>

  • Google analytics 4 (bigquery export)

    GA4 allows systematic sampling when analyzing exported data in BigQuery using SQL queries.

    • Sample query:

      Run a query to select every 10th event record:

      SELECT *
FROM `my_project.analytics_dataset.events_*`
WHERE MOD(ROW_NUMBER() OVER (ORDER BY event_timestamp), 10) = 1;

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