One of the most common questions in A/B testing is: "How long should I run my experiment?" Running tests for the right duration is critical—stop too early and you risk making decisions on unreliable data; run too long and you waste time and opportunity. This guide provides clear frameworks for determining optimal experiment duration.
Never end an experiment based solely on elapsed time. The primary factor determining when to conclude a test is statistical significance—the confidence that observed differences aren't due to random chance.
PressPlay automatically calculates statistical significance for your experiments. Wait until your experiment reaches at least 95% confidence before making decisions.
95% confidence: 95% probability that the observed difference is real, not random
99% confidence: Higher certainty, recommended for major decisions
Below 95%: Results are inconclusive—continue testing or declare no winner
Regardless of statistical significance, run every experiment for at least 7 full days to account for day-of-week variations:
Weekly patterns: User behavior differs between weekdays and weekends
Complete cycles: Capture full weekly shopping and download patterns
Day-specific effects: Avoid bias from launching on a particular day
Example: A test that reaches significance on day 3 (Thursday) might show different results after weekend traffic is included. Always wait for the full 7-day cycle.
Traffic volume dramatically affects how quickly experiments reach significance:
Expected duration: 7-14 days
Considerations: Can detect smaller effect sizes quickly
Strategy: Test more frequently, iterate rapidly
Expected duration: 14-28 days
Considerations: Need moderate effect sizes to detect reliably
Strategy: Focus on high-impact changes, be patient
Expected duration: 28-56 days
Considerations: Only large effect sizes will be detectable
Strategy: Test dramatic differences, consider qualitative methods too
Expected duration: May not reach significance
Considerations: A/B testing may not be the right optimization method
Strategy: Use user research, competitor analysis, and best practices instead
Check your weekly impression count in PressPlay's analytics dashboard to set appropriate expectations.
Beyond duration, you need sufficient sample size for reliable results:
Per variant: At least 100 impressions minimum, 1,000+ ideal
Total experiment: 200+ impressions across all variants
For conversions: At least 20-30 conversions per variant to detect meaningful differences
Even with 7+ days elapsed, low sample sizes produce unreliable results. If your experiment has run for weeks but only accumulated 50 impressions per variant, the results aren't trustworthy regardless of what the numbers show.
Several scenarios warrant extending experiments beyond typical durations:
When variants perform similarly (within 2-3% of each other), run longer to detect subtle differences:
Extend by 1-2 weeks beyond reaching minimum duration
If still inconclusive, larger sample won't help—declare no significant difference
For major changes (new brand identity, complete messaging overhaul), run longer for higher confidence:
Aim for 99% confidence instead of 95%
Run for 3-4 weeks minimum
Collect qualitative feedback alongside quantitative data
During volatile periods, extend tests to capture representative data:
Holiday testing: Run through entire holiday period plus normal period
Launch periods: If testing during/after major app update, extend duration
Promotional campaigns: Account for artificial traffic spikes
If unusual events occur during testing, extend to gather clean data:
Competitor launches or major promotions in your category
Google Play algorithm or UI changes
Unexpected press coverage or viral attention
Technical issues affecting app store visibility
While you should generally avoid stopping early, some situations warrant it:
Severe performance degradation: A variant performs dramatically worse (20%+ decline) with high confidence after minimum duration
Technical errors: You discover the experiment was misconfigured or assets were incorrect
Platform policy violations: Google flags assets as violating policies
Business circumstances change: Major app update or pivot makes experiment irrelevant
A variant is winning after 2-3 days: Too early to be certain
Impatience: Want to start next test sooner
Executive pressure: Leadership wants to see results quickly
Traffic lower than expected: If it's not reaching significance, either wait longer or accept inconclusive results
For most experiments, implement this decision framework at the 2-week mark:
Has statistical significance been reached (95%+)?
Yes → Is there a clear winner? → Implement and end test
Yes → Results too close? → Run 1 more week, then decide
No → Continue to step 2
Is sample size sufficient (1,000+ impressions per variant)?
Yes → Run 1-2 more weeks maximum, then accept results
No → Check if you'll reach sufficient sample → Continue or pause
Are external factors affecting the test?
Yes → Extend past the external factor, then reassess
No → Continue for 1-2 more weeks maximum
Set a hard maximum of 8 weeks for any experiment. If you haven't reached conclusive results by then:
Low sample size issue: Your app doesn't have sufficient traffic for reliable A/B testing of this element
Variants too similar: The difference between variants is too small to detect
High variance: Your metric has too much natural fluctuation
In these cases, either declare no significant difference (implement whichever variant you prefer) or try a more dramatically different variation.
Testing more than two variants requires longer duration:
2 variants (A/B): Standard duration as outlined above
3 variants (A/B/C): Add 25-50% to standard duration
4+ variants: Add 50-100% to standard duration, or requires very high traffic
With multiple variants, traffic splits across more options, requiring more time to achieve the same statistical confidence per variant.
Based on typical durations, plan your testing roadmap:
Q1: 6-8 experiments (2 weeks each)
Q2-Q4: 18-24 more experiments
Annual total: 24-32 optimization cycles
Q1: 3-4 experiments (3 weeks each)
Q2-Q4: 9-12 more experiments
Annual total: 12-16 optimization cycles
Understanding realistic timelines helps set stakeholder expectations and plan resources accordingly.
✓ Never end test before 7 full days
✓ Wait for 95%+ statistical confidence
✓ Ensure minimum 1,000 impressions per variant (ideally)
✓ Account for day-of-week patterns
✓ Set maximum duration of 8 weeks
✓ Check results at 2-week milestone
✓ Document any external factors affecting test
✓ Adjust expectations based on traffic level
✓ Add time for multi-variant tests
✓ Consider seasonal factors in timing
Peeking constantly: Checking results every day and making premature decisions
Inconsistent standards: Ending some tests early but running others too long
Ignoring sample size: Focusing only on elapsed time, not data volume
Testing during holidays: Starting tests right before major holidays
Confirmation bias: Stopping when results match expectations, continuing when they don't
By following these duration guidelines, you'll ensure your experiments run long enough to produce reliable, actionable results while maintaining an efficient testing cadence.
