Avoiding testing biases

Even carefully designed tests can produce misleading results when biases creep in. Testing bias occurs when factors other than your test variables influence the outcome, compromising the validity of your experiments.

Common testing biases to watch for include:

• Selection bias: When your test subjects aren't representative of your actual user base. For example, testing only with power users skews results toward expert behavior patterns.
• Timing bias: Seasonal factors or time-based events can affect user behavior. Testing during holidays or special events may produce results that don't represent normal conditions.
• Novelty effect: Users often respond differently to new features simply because they're new, not because they're better. Initial excitement can inflate performance metrics temporarily.
• Order bias: The sequence in which variants are presented can influence results, especially in usability testing, where learning affects subsequent interactions.

To minimize these biases:

• Use proper randomization when assigning users to test groups to ensure representative samples.
• Run tests for adequate durations to account for day-of-week variations and novelty effects.
• Establish control groups that receive no changes to benchmark against external factors affecting all users.
• Segment your analysis to verify consistent performance across different user types and conditions.

Eliminating all bias is impossible, but recognizing and accounting for potential sources of bias leads to more trustworthy test results and better product decisions.^[1]