A/B testing is meant to provide clear answers, but what happens when your experiment ends without a winner? Inconclusive results frustrate marketers and CRO professionals everywhere—yet they’re more common than you might think.
This guide explains why inconclusive results happen, how to interpret them, and most importantly, what steps to take when your tests fail to deliver a clear outcome.
Understanding Inconclusive Results
An A/B test is inconclusive when it fails to achieve statistical significance, meaning you can’t confidently say which variation performs better. Statistically, this happens when:
- The chance of a winner falls below 90-95%.
- The p-value exceeds 0.05.
- The confidence interval includes zero.
However, statistical significance isn’t a universal standard. It’s a threshold you set before running the test, one that should align with your goals and context. Many use 95%, but that number isn’t always appropriate. Your threshold should balance statistical rigor with business needs.
Why Tests Fail to Deliver Clear Results
Minimal Impact
Sometimes your changes don’t meaningfully affect user behavior. Possible reasons include:
- The change is too subtle.
- It doesn’t solve a real user problem.
- The hypothesis was off.
- It appeals to some users but turns others away.
Insufficient Sample Size
Low-traffic websites often struggle with A/B tests because smaller sample sizes make it harder to detect differences. Most tests need hundreds of conversions per variation to be reliable.
Poor Timing
Tests can fail if they’re not run long enough or are conducted during unusual periods, like holidays or promotions. Stopping a test early based on promising results is another common mistake.
External Influences
Unexpected factors like marketing campaigns, seasonal changes, site bugs, or competitor actions can skew results.
Interpreting the Numbers
Confidence Intervals
Confidence intervals give the clearest picture of your results. For example, if a test shows a lift between -2% and +8%, the worst case is a 2% drop, and the best case is an 8% gain. Even without statistical significance, this helps you decide whether to move forward.
P-Values
A p-value tells you the chance your results happened by random chance. For instance, a p-value of 0.08 means there’s an 8% chance your results are random. However, p-values don’t reveal how meaningful the difference is for your business.
Statistical Power
Statistical power shows your test’s ability to detect true effects. Many tests fail because they lack power due to small sample sizes. Before testing, calculate the minimum detectable effect (MDE) to understand the smallest change you can reliably measure.
What to Do Next
Revisit Your Hypothesis
Reevaluate your initial idea. Was it based on user research and data? Could an opposite approach work better? Strong hypotheses are specific, testable, and grounded in user behavior.
Extend the Test
If confidence intervals show potential improvement, consider running the test longer. But avoid p-hacking by setting clear extension criteria and focusing on confidence intervals over p-values.
Refine Your Variations
If the changes were too minor to make an impact, try:
- Bigger changes to increase effect size.
- Testing the opposite idea.
- Targeting a different problem.
- Combining multiple small adjustments.
Segment Your Data
Flat overall results might hide important trends within user segments, such as:
- New vs. returning visitors.
- Traffic sources.
- Geographic regions.
- Devices.
Segmenting data can reveal that a variation works well for certain groups, allowing for targeted strategies.
Try Alternative Methods
Traditional A/B testing isn’t always the best fit. Consider:
- Multivariate testing for multiple changes at once.
- Sequential testing for ongoing analysis.
- Bandit algorithms to dynamically adjust traffic.
- Qualitative research to explore user behavior.
Leveraging AI for Better Results
AI-powered platforms like ezbot.ai overcome many challenges of traditional A/B testing by:
- Testing multiple variations simultaneously.
- Automatically reallocating traffic to better-performing options.
- Personalizing experiences for different user groups.
- Learning continuously to adapt to changing user preferences.
These tools save time, require less traffic, and deliver faster results, making them ideal for smaller businesses.
Moving Forward
Document Learnings
Even inconclusive results provide valuable insights. Record:
- What you tested and why.
- The observed results.
- User behavior insights.
- Hypotheses for future tests.
This builds knowledge and avoids repeating ineffective tests.
Build a Testing Program
Create a structured optimization program that:
- Prioritizes high-impact tests.
- Builds on past findings.
- Tests multiple aspects of the user experience.
- Balances quick wins with long-term improvements.
Focus on Practical Impact
Prioritize business impact over statistical significance. A small but statistically significant improvement might be less valuable than a larger, inconclusive result suggesting potential for growth.
"Negative" Results Are Still Wins
Inconclusive or negative results aren’t failures—they’re learning opportunities. They help you refine hypotheses, understand user behavior, and improve future tests. In science, negative results advance knowledge; the same applies to CRO.
By embracing AI and modern optimization techniques, you can move past the frustrations of inconclusive tests and unlock better results for your business.
