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Avoiding Common Mistakes

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Updated
ByEmily Gomez
A/B testing is most useful when each experiment is designed to answer a clear question.
Avoiding a few common mistakes can make your results easier to interpret and help you get more useful insights from Bluvia A/B Testing.

Testing Without a Clear Hypothesis

Avoid creating an experiment simply because you want to see whether something different performs better.
Before launching, define:
  • What you’re changing.
  • Why you think the change may help.
  • What visitor behavior you expect to change.
  • How you’ll measure the outcome.
Add this information to Notes & Hypothesis so you can revisit your original reasoning when reviewing the results.

Choosing the Wrong Conversion Goal

Your Conversion Goal should directly relate to what you’re trying to improve.
For example, if you’re testing whether new CTA copy generates more clicks, Click on Element provides a more direct measurement than general page engagement.
If you’re trying to increase completed form submissions, Submit a Form is more meaningful than measuring clicks on the button leading to the form.
Choose the goal that best represents success for the experiment.
See Choosing Good Conversion Goals for more guidance.

Changing Too Much at Once

Changing several unrelated elements in the same Variant can make it difficult to understand why one experience performed better.
If you change the headline, CTA, navigation, imagery, and layout at the same time, you may determine which overall experience performed betterā€”but not which individual change caused the difference.
Keep Variants focused when your goal is to understand the impact of a specific change.
Broader changes can still be useful when you’re intentionally comparing two complete experiences.

Stopping a Test Too Early

Don’t stop an experiment simply because one Variant takes an early lead.
Results can change significantly as more visitors participate.
Before making a decision, consider:
  • Statistical confidence.
  • Sample size.
  • Number of conversions.
  • How long the experiment has been running.
  • Whether the test has covered a representative traffic period.
See Running Statistically Valid Tests and How Long Should a Test Run? for more guidance.

Treating Statistical Confidence as the Only Answer

Bluvia A/B Testing defaults to a 95% confidence level, and you can adjust this threshold when configuring an experiment.
Confidence is an important signal, but it shouldn’t be considered in isolation.
A test with limited traffic or very few conversions may still need additional data. Similarly, a statistically confident improvement may be too small to have meaningful practical value.
Review the complete experiment results before declaring a winner.

Running Overlapping Experiments

Bluvia A/B Testing allows you to run multiple experiments simultaneously, but avoid tests that could interfere with one another.
Experiments may overlap when:
  • They modify the same page or component.
  • One experiment changes an element inside an area being tested by another experiment.
  • Both tests could influence the same visitor action.
  • One experiment could affect the Conversion Goal of another.
For example, running a homepage hero experiment while separately testing the CTA inside that hero can make it harder to determine which change influenced the result.
If two experiments could affect each other’s outcomes, run them separately.

Editing an Experiment Mid-Test

Avoid making significant changes to an experiment after it has started collecting data.
Changing a Variant, Conversion Goal, or other important configuration can make the results before and after the change difficult to compare.
Editing an Active experiment in Bluvia A/B Testing will pause the test.
If the change substantially alters what you’re testing, consider duplicating the experiment and creating a new test instead.

Testing Your Own Visits Without Considering Exclusions

When checking an experiment yourself, remember that your own activity may be excluded from the test.
For example, your experiment may exclude:
  • Logged-in users.
  • Administrator traffic.
  • Other visitors covered by your Exclusion Rules.
Bluvia A/B Testing also uses cookies to maintain a visitor’s assignment to a Control or Variant.
Use Preview when you need to verify a specific Variant rather than repeatedly refreshing the live experiment.

Ignoring Technical Issues

Don’t assume unexpected behavior is part of the experiment.
If a Variant doesn’t display correctly or conversions aren’t being recorded, investigate the issue before allowing the test to continue collecting data.
Common areas to check include:
  • Caching.
  • CDN configuration.
  • JavaScript conflicts.
  • Targeting and Exclusion Rules.
  • Conversion Goal configuration.
Pause the experiment while troubleshooting when necessary.

Assuming Every Test Needs a Winning Variant

Not every experiment will produce a Variant that clearly outperforms the Control.
Sometimes:
  • The Control performs better.
  • The difference is too small to matter.
  • The experiment remains inconclusive.
  • The hypothesis isn’t supported.
Those outcomes are still useful.
The purpose of experimentation isn’t to prove that every proposed change is better. It’s to learn how visitors respond so you can make more informed optimization decisions.

Learn From Each Experiment

Keep completed and useful experiments in your Experiment History.
Over time, previous tests can help you identify patterns, avoid repeating unsuccessful ideas, and develop stronger hypotheses.
A good experimentation program builds on what you’ve already learned rather than treating every test as an isolated project.

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