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A/B Testing Results Trust & Reliability Survey Template

Measure trust in A/B test results, identify flaky experiments, and boost data quality. Launch this survey to spot issues and build a stronger testing culture.

What's Included

AI-Powered Questions

Intelligent follow-up questions based on responses

Automated Analysis

Real-time sentiment and insight detection

Smart Distribution

Target the right audience automatically

Detailed Reports

Comprehensive insights and recommendations

Sample Survey Items

Q1
chat message
Thank you for participating. Responses are confidential and analyzed in aggregate. Please be candid.
Q2
multiple choice
Which areas best describe your role? Select up to three.
  • Product Management
  • Engineering
  • Data Science / Analytics
  • Design / UX
  • Marketing / Growth
  • Operations / Support
  • Leadership / Strategy
  • Other
Q3
multiple choice
In the last 6 months, how often have you consumed or acted on A/B test results?
  • Weekly or more
  • 1 to 3 times per month
  • A few times total
  • Not in the last 6 months
  • Never
Q4
opinion scale
Based on what you see and hear, how reliable are our A/B results overall?
Q5
multiple choice
What limits your use of A/B test results today? Select all that apply.
  • Hard to access results
  • Unsure how to interpret results
  • Don’t trust data quality
  • Not relevant to my work
  • No tests run in my area
  • Lack of time
  • Other
Q6
multiple choice
Would a brief primer on power, minimum detectable effect (MDE), and uncertainty be helpful to you?
  • Yes
  • Maybe
  • No
Q7
numeric
About how many distinct A/B tests did you work on or consume results from in the last 3 months?
Q8
multiple choice
Where are the tests you touch primarily run? Select all that apply.
  • Web
  • iOS app
  • Android app
  • Backend systems
  • Marketing channels (email/ads)
  • Other
Q9
opinion scale
How much do you trust the validity of our A/B test conclusions lately?
Q10
multiple choice
Recently, have you observed flaky or inconsistent A/B outcomes on key metrics?
  • No
  • Yes, occasionally
  • Yes, frequently
  • Unsure
Q11
long text
Please share one or two recent examples of flakiness and what you think caused them.
Max 600 chars
Q12
matrix
How often do the following contribute to flaky results in your area?
Q13
dropdown
When deciding to ship based on a test, what effect size on the primary metric is typically meaningful for you?
Q14
rating
How often do A/B results meaningfully change your team’s decisions?
Q15
opinion scale
How clear is the communication of uncertainty (confidence intervals, p-values, power) in shipped reports?
Q16
multiple choice
Before launch, how often are MDE and power planned explicitly?
  • Always
  • Often
  • Sometimes
  • Rarely
  • Never
  • Unsure
Q17
ranking
Rank the top improvements that would most increase your trust.
Q18
multiple choice
Attention check: To confirm you’re paying attention, please select “Often” here.
  • Never
  • Rarely
  • Sometimes
  • Often
  • Always
Q19
dropdown
How long have you been at the company?
Q20
dropdown
Total years of professional experience
Q21
multiple choice
Where are you primarily located?
  • Americas
  • Europe
  • Middle East & Africa
  • Asia-Pacific
  • Multiple regions
  • Prefer not to say
Q22
dropdown
What is your seniority level?
Q23
multiple choice
Which product area(s) do you mostly support? Select up to three.
  • Consumer-facing experience
  • B2B / Enterprise
  • Infrastructure / Platform
  • Monetization / Payments
  • Marketing / Growth
  • Internal tools
  • Other
  • Prefer not to say
Q24
long text
Anything else we should know about trust or flakiness in our experiments?
Max 600 chars
Q25
ai interview
AI Interview: 2 Follow-up Questions on A/B Testing Trust and Flakiness
AI Interview
Q26
chat message
Thanks for your time—your feedback will help us improve our experimentation quality and communication.

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A/B Testing Results Trust & Reliability Survey Template - Survey Template | QuestionPunk