What is Experimental Design? Examples and Best Practices

Experimental design

A systematic approach to planning, running, and analyzing tests in analytics to derive statistically valid insights.

Introduction to Experimental Design

• Hypothesis formulation

  Defining a clear, testable hypothesis that predicts how changes will affect user behavior or key metrics.

  • Null hypothesis:

    Assumes there is no effect or difference between variants.

  • Alternative hypothesis:

    Specifies the expected effect or difference to be tested.

• Variable identification

  Distinguishing between independent, dependent, and controlled variables to structure the experiment.

  • Independent variable:

    The element you manipulate, such as button color.

  • Dependent variable:

    The outcome measured, such as click-through rate.

  • Controlled variables:

    Factors kept constant to prevent confounding effects.

• Randomization and control

  Ensuring unbiased assignment of users to control and treatment groups.

  • Random assignment methods:

    Techniques like hashing user IDs or using existing user attributes.

  • Control group:

    A baseline group that experiences the original version.

Types of Experimental Designs

• A/b testing

  Compares two versions (A vs. B) to see which performs better.

• Multivariate testing

  Simultaneously tests multiple elements to identify the best combination.

• Factorial design

  Tests multiple factors and their interactions at different levels.

Implementing Experiments with Analytics Tools

• Plainsignal (cookie-free analytics)

  Use PlainSignal to deploy experiments without relying on cookies, ensuring user privacy and compliance.

  • Setup tracking code:

    Include the following snippet in your HTML to start tracking:

    <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>
    

  • Define event goals:

    Configure events, such as clicks or conversions, to measure experiment outcomes.

  • Segment users:

    Assign users randomly to control or treatment groups within the PlainSignal dashboard.

• Google analytics 4 (ga4)

  Leverage GA4’s built-in experimentation features to run and analyze tests.

  • Ga4 tracking setup:

    Add this snippet to enable GA4 tracking:

    <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');
    </script>
    

  • Create experiments:

    Use the ‘Experiments’ section in GA4 to set up A/B or multivariate tests.

  • Analyze results:

    Review test reports in GA4 to determine statistical significance and impact.

Best Practices and Common Pitfalls

• Sample size and statistical power

  Calculate the minimum sample needed to detect meaningful differences and achieve reliable results.

• Test duration

  Run experiments long enough to account for variations in traffic and user behavior.

• Avoiding bias

  Prevent selection and measurement biases through proper randomization and consistent data collection.

• Data privacy and compliance

  Ensure experiments adhere to privacy laws and internal policies, especially when using user data.

Real-World Example: Landing Page Button Color Test

• Scenario setup

  An e-commerce site wants to test whether a red ‘Buy Now’ button yields a higher click-through rate than a blue one.

• Implementation in plainsignal

  Use PlainSignal’s event tracking to record clicks for each variant and ensure random user assignment.

• Data analysis

  Compare click-through rates, calculate statistical significance, and decide whether to adopt the new button color.

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