Published on 2025-06-26T05:35:50Z

What is Confirmatory Analysis? Examples in Analytics

Confirmatory Analysis is the systematic process of testing predefined hypotheses using statistical methods to verify if observed metrics and user behaviors are significant or due to chance within the context of web analytics. It requires that hypotheses, test criteria, and analysis procedures are established before examining the data, reducing the risk of biased results, p-hacking, and false discoveries. In practice, Confirmatory Analysis follows phases including hypothesis formulation, data collection, statistical testing, and interpretation, ensuring decisions are based on robust and replicable evidence. Tools like Plainsignal and Google Analytics 4 (GA4) facilitate this process by enabling accurate event tracking, data segmentation, and built-in analysis features. By contrasting with Exploratory Analysis, which seeks to uncover new insights, Confirmatory Analysis provides a disciplined framework for validating specific business questions and informing actionable strategies.

Illustration of Confirmatory analysis
Illustration of Confirmatory analysis

Confirmatory analysis

Statistically testing predefined web analytics hypotheses to validate real effects and avoid false discoveries.

Definition and Purpose

Definition and contrast with exploratory approaches.

  • Confirmatory vs exploratory

    Confirmatory Analysis tests predefined hypotheses, while Exploratory Analysis searches for patterns without prior assumptions.

Key Steps in Confirmatory Analysis

Overview of the main phases in a structured confirmatory workflow.

  • Formulating hypotheses

    Define clear, falsifiable statements about user behavior or metrics before examining the data.

    • Null hypothesis (h0):

      The default assumption that there is no effect or difference.

    • Alternative hypothesis (h1):

      The statement you aim to support, indicating an effect or difference.

  • Data collection and preparation

    Gather, clean, and ensure the quality of data using analytics tools.

    • Data quality checks:

      Verify accuracy, completeness, and consistency of the dataset.

    • Sampling considerations:

      Ensure sample size and selection methods support statistical validity.

  • Statistical testing

    Apply tests like t-tests, chi-square, or non-parametric methods depending on data type.

    • P-values:

      Measure the probability of observing results at least as extreme as those measured, under H0.

    • Confidence intervals:

      Range of values within which the true effect size is expected to lie.

    • Effect sizes:

      Quantify the magnitude of the observed effect.

  • Interpretation and decision

    Determine whether to accept or reject hypotheses and translate findings into actions.

    • Significance thresholds:

      Commonly a p-value < 0.05 is used, but should be chosen based on context.

    • Business context:

      Align statistical results with business objectives and stakeholder requirements.

Implementing Confirmatory Analysis with Plainsignal and GA4

Step-by-step integration and example tracking code for PlainSignal and Google Analytics 4.

  • Plainsignal setup

    Add PlainSignal’s cookie-free analytics script to your HTML to begin simple event and pageview tracking.

    • Script integration:

      Insert the following in the <head> of your HTML to initialize 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>
    • Custom event tracking:

      Use PlainSignal’s API methods to send custom events for your confirmatory tests (e.g., button clicks, form submissions).

  • Ga4 setup

    Configure GA4 to track events and leverage its analysis features for confirmatory tests.

    • Gtag event snippet:

      Example GA4 setup and event tracking snippet:

      <script async src="https://www.googletagmanager.com/gtag/js?id=GA_MEASUREMENT_ID"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);} 
  gtag('js', new Date());
  gtag('config', 'GA_MEASUREMENT_ID');
  // Example event
  gtag('event', 'button_click', {
    'event_category': 'CTA',
    'event_label': 'Signup Button'
  });
</script>
    • Using ga4 reports and explorations:

      Leverage GA4’s Explorations and built-in statistical tools to compare groups and validate hypotheses.

Best Practices and Limitations

Guidelines to ensure robust, reliable results and awareness of potential constraints.

  • Ensure adequate sample size

    Calculate statistical power and minimum required sample size to detect expected effects.

  • Avoid p-hacking

    Predefine your analysis plan and stick to hypothesis tests to prevent false positives.

  • Account for external factors

    Consider seasonality, marketing campaigns, and technical changes that might influence data.

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