What is Experiment Design in Analytics? Examples and Best Practices

Experiment design

Structured process to plan and analyze analytics experiments, from hypotheses to implementation using Plainsignal and GA4.

Why Experiment Design Matters

• Valid results

  Ensures conclusions reflect true effects rather than random variation.

  • Statistical significance:

    Probability that observed effects are not due to chance.

  • Confidence intervals:

    Range within which the true effect size likely falls.

• Risk mitigation

  Reduces false positives (Type I error) and false negatives (Type II error), saving time and budget.

  • Type i error:

    False positive finding an effect when none exists.

  • Type ii error:

    False negative missing a true effect.

• Data-driven decisions

  Provides empirical evidence to guide optimizations and strategy.

  • Iteration:

    Refining experiments based on previous results.

  • Continuous improvement:

    Ongoing testing to steadily improve performance.

Key Components of Experiment Design

• Hypothesis formulation

  Articulate a clear, testable statement predicting the effect of a change.

  • Null hypothesis:

    Baseline assumption of no effect.

  • Alternative hypothesis:

    Expected effect or difference.

• Variable identification

  Determine independent variables (what you change) and dependent variables (what you measure).

  • Independent variables:

    Elements manipulated in the experiment.

  • Dependent variables:

    Metrics observed for changes.

  • Control variables:

    Factors held constant to avoid confounding effects.

• Sample size determination

  Calculate how many participants or sessions you need to detect effects with sufficient statistical power.

  • Power analysis:

    Assess the probability of detecting an effect of a given size.

  • Minimum detectable effect:

    Smallest effect size worth detecting.

• Randomization & assignment

  Randomly assign users to control or test groups to eliminate selection biases.

  • Random assignment:

    Ensures each user has an equal chance of receiving any variant.

  • Stratification:

    Maintain balanced segment distribution across variants.

Types of Experiments in Analytics

• A/b testing

  Compare two versions of a page or feature (Control vs Variant) to see which performs better.

  • Control group:

    Users exposed to the original version.

  • Variant group:

    Users exposed to the modified version.

• Multivariate testing

  Test multiple elements simultaneously to understand interaction effects.

  • Factorial designs:

    All possible combinations of variables are tested.

  • Interaction effects:

    How changes in one variable may influence another.

• Split url testing

  Redirect users to entirely different URLs to compare full-page redesigns.

  • Redirect tests:

    Use server-side or client-side redirects to route traffic.

Implementing Experiments with SaaS Tools

• Plainsignal

  A cookie-free, lightweight analytics tool ideal for privacy-focused A/B testing. Embed the script:

  <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>
  

  Then track events:

  PlainSignal.event('experiment_view', {
    experiment_id: 'exp123',
    variant: 'A'
  });
  

• Google analytics 4

  Use GA4’s event-based model to capture experiment data. Configure GA4 in your page header:

  <script async src="https://www.googletagmanager.com/gtag/js?id=G-XXXXXXX"></script>
  <script>
    window.dataLayer = window.dataLayer || [];
    function gtag(){dataLayer.push(arguments);} 
    gtag('js', new Date()); 
    gtag('config', 'G-XXXXXXX');
  </script>
  

  Then send a custom event for variant exposure:

  gtag('event', 'experiment_view', {
    experiment_id: 'exp123',
    variant: 'B'
  });
  

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