Published on 2025-06-28T00:10:42Z

What is Sample Size in Analytics? Examples for Sample Size.

Sample size refers to the number of individual observations or data points collected to represent a larger population in web analytics. It directly affects the reliability and validity of insights: larger sample sizes tend to produce more stable and accurate metrics, while small samples can lead to misleading conclusions due to random variation.

In analytics platforms like Plainsignal (a cookie-free, simple analytics tool) and Google Analytics 4 (GA4), understanding sample size is crucial for interpreting reports correctly and planning experiments. Plainsignal delivers full-traffic data without sampling thresholds, whereas GA4 may apply sampling to large datasets, impacting metric precision.

Example integration with Plainsignal:

<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>

Example integration with GA4:

<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-XXXXXXXXXX"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);}  gtag('js', new Date());
  gtag('config', 'G-XXXXXXXXXX');
</script>
Illustration of Sample size
Illustration of Sample size

Sample size

The number of data points collected in web analytics to ensure statistically reliable insights.

Definition and Importance

Explains what sample size is in the context of web analytics and why it matters for data accuracy and insight reliability.

  • Population vs. sample

    The population includes all website visitors or events, while the sample is the subset actually collected and analyzed.

  • Impact on accuracy

    A larger sample minimizes random error, making metrics like conversion rates and session duration more stable.

Calculating Sample Size

Outlines methods to estimate how many data points are needed for valid analysis, balancing confidence levels and margins of error.

  • Statistical formulas

    Uses confidence levels, expected variance, and margin of error to compute the minimum sample size required.

  • Practical heuristics

    Common rules of thumb include collecting at least 1,000 sessions or 200 conversions per variant in A/B tests.

Sample Size in Different Analytics Platforms

Describes how different platforms handle sampling and how sample size affects reports in PlainSignal and GA4.

  • Plainsignal

    Provides unsampled, cookie-free analytics by processing all events without thresholds, ensuring full data coverage.

  • Google analytics 4

    Applies sampling in some explorations and reports when data volume exceeds limits, potentially introducing variance if sample size is insufficient.

Best Practices

Recommendations to ensure adequate sample sizes when collecting and analyzing web data.

  • Plan data collection

    Define your target sample size in advance based on traffic forecasts and test goals to avoid underpowered analyses.

  • Monitor sampling in ga4

    Regularly check GA4’s sampling indicators in explorations and adjust date ranges or filters to increase sample size as needed.

  • Aggregate data over time

    Combine data across longer periods or similar segments to boost sample size for more reliable insights.

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