All Templates

A/B Experimentation Trust & Data Quality Assessment

An internal diagnostic survey for teams that run or consume A/B tests, measuring trust in experiment results, identifying sources of flakiness, and prioritizing process and tooling improvements.

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

Template Overview

27

Questions

AI-Powered

Smart Analysis

Ready-to-Use

Launch in Minutes

This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.

Sample Survey Items

Q1
Chat Message
Welcome to the Experimentation Trust & Quality Survey. We're gathering candid feedback on how A/B test results are used and trusted across the organization. Your responses are confidential and will be reported only in aggregate — there are no right or wrong answers. Participation is voluntary, and you may exit at any time. The survey takes approximately 6–8 minutes. Results will be used internally to improve our experimentation practices and communication.
Q2
Multiple Choice
Which functional 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 reviewed 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
Chat Message
The following questions are for those who have not actively used A/B test results recently. If you regularly work with test results, you may skip ahead.
Q5
Opinion Scale
Based on your general impression, how reliable are our A/B test results overall?
Range: 1 – 7
Min: Not at all reliableMid: NeutralMax: Extremely reliable
Q6
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 the data quality
  • Not relevant to my work
  • No tests run in my area
  • Lack of time
  • Other
Q7
Opinion Scale
How useful would a short guide explaining key experimentation concepts (e.g., statistical power, minimum detectable effect, confidence intervals) be for your work?
Range: 1 – 7
Min: Not at all usefulMid: NeutralMax: Extremely useful
Q8
Chat Message
The following questions are for those who have actively worked with A/B test results in the past 3–6 months.
Q9
Dropdown
Approximately how many distinct A/B tests did you work on or review results from in the last 3 months?
  • 1–2
  • 3–5
  • 6–10
  • 11–20
  • More than 20
Q10
Multiple Choice
Where are the A/B tests you work with primarily run? (Select all that apply.)
  • Web
  • iOS app
  • Android app
  • Backend systems
  • Marketing channels (email / ads)
  • Other
Q11
Opinion Scale
How much do you trust the validity of our A/B test conclusions over the past 3 months?
Range: 1 – 7
Min: Do not trust at allMid: NeutralMax: Trust completely
Q12
Multiple Choice
How often do A/B test results meaningfully change your team's decisions?
  • Almost always
  • Often
  • Sometimes
  • Rarely
  • Almost never
Q13
Multiple Choice
In the past 3 months, have you observed flaky or inconsistent A/B test outcomes on key metrics?
  • No
  • Yes, occasionally
  • Yes, frequently
  • Unsure
Q14
Long Text
If you observed flaky or inconsistent outcomes, please share one or two examples and what you think caused them.
Max chars
Q15
Multiple Choice
How often do each of the following contribute to flaky or unreliable A/B test results in your area?
  • Insufficient sample size or test duration
  • Instrumentation or logging bugs
  • Peeking at results before reaching significance
  • Interactions between concurrent experiments
  • Unstable or delayed data pipelines
  • Poorly defined or overly sensitive metrics
  • External events or seasonality
  • Other
Q16
Opinion Scale
How clearly do shipped experiment reports communicate uncertainty (e.g., confidence intervals, statistical significance)?
Range: 1 – 7
Min: Not at all clearMid: NeutralMax: Extremely clear
Q17
Multiple Choice
Before launch, how often are minimum detectable effect (MDE) and statistical power planned explicitly for experiments?
  • Always
  • Often
  • Sometimes
  • Rarely
  • Never
  • Unsure
Q18
Dropdown
When deciding to ship based on a test result, what minimum effect size on the primary metric is typically meaningful for your team?
  • It depends on context
  • Any positive change
  • At least 0.5 percentage points
  • At least 1 percentage point
  • At least 2 percentage points
  • At least 5 percentage points
Q19
Ranking
Rank the following improvements by how much they would increase your trust in A/B test results. (Drag to reorder; most impactful first.)
Drag to order (top = most important)
  1. Better instrumentation and QA
  2. Guardrails against peeking at results early
  3. Faster and more stable data pipelines
  4. Pre-registration of hypotheses and metrics
  5. Automated power / MDE checks before launch
  6. Clearer result summaries and decision guidance
Q20
AI Interview
Based on your responses in this survey, please share any additional thoughts or concerns about the trustworthiness or reliability of our A/B testing program.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 5
Q21
Chat Message
Finally, a few questions about your background for analysis purposes.
Q22
Dropdown
How long have you been at the company?
  • Less than 6 months
  • 6 to 12 months
  • 1 to 2 years
  • 3 to 5 years
  • More than 5 years
Q23
Dropdown
How many years of total professional experience do you have?
  • 0 to 2
  • 3 to 5
  • 6 to 10
  • 11 to 15
  • More than 15
Q24
Dropdown
What is your seniority level?
  • Individual contributor
  • People manager
  • Director+
  • Prefer not to say
Q25
Multiple Choice
Where are you primarily located?
  • Americas
  • Europe
  • Middle East & Africa
  • Asia-Pacific
  • Multiple regions
  • Prefer not to say
Q26
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
Q27
Chat Message
Thank you for your time. Your feedback will directly inform improvements to our experimentation practices, tooling, and communication. Results will be shared in aggregate with the broader team.

Frequently Asked Questions

What is QuestionPunk?
QuestionPunk is a lightweight survey platform for live AI interviews you control. It's fast, flexible, and scalable—adapting every question in real time, moderating responses across languages, letting you steer prompts, models, and flows, and even generating surveys from a simple prompt. Get interview-grade insight with survey-level speed across qual and quant.
How do I create my first survey?
Sign up, then decide how you want to build: let the AI generate a survey from your prompt, pick a template, or start from scratch. Choose question types, set logic, and preview before sharing.
How can I share surveys with my team?
Send a project link so teammates can view and collaborate instantly.
Can the AI generate a survey from a prompt?
Yes. Provide a prompt and QuestionPunk drafts a survey you can tweak before sending.
How long does support typically take to reply?
We reply within 24 hours—often much sooner. Include key details in your message to help us assist you faster.
Can I export survey results?
Absolutely. Export results as CSV straight from the results page for quick data work.

Related Templates

View All Templates

Ready to Get Started?

Launch your survey in minutes with this pre-built template

Use This TemplateBrowse More Templates