A/B Testing Readout Agent

The description states that A/B Testing Readout Agent is a tool built by one developer to automate statistical analysis of A/B test results. The author claims it runs appropriate statistical tests, checks assumptions, and generates plain-English readouts with recommendations. It uses Codex for development and integrates GPT-5.6 for narration. The project appears to be a solo hackathon effort with no evidence of revenue, customers or traction.

The single most important open question is whether the tool actually delivers on its claim of running correct statistical tests that would replace a statistician's role — this cannot be verified from the self-reported description alone.

The description states that A/B Testing Readout Agent:

• Takes raw experiment data in CSV format with variant and outcome columns
• Auto-detects whether outcome is binary or continuous
• Runs appropriate statistical tests (two-proportion z-test for binary, Welch's/pooled t-test for continuous)
• Reports effect size, confidence intervals, and achieved-power estimates
• Performs pitfall checks including minimum sample size, group imbalance, extreme conversion rates, and sample-ratio-mismatch testing
• Produces a conservative ship/do-not-ship/inconclusive recommendation
• Uses GPT-5.6 for two-step narration: first draft in plain English, then review against raw statistics
• Logs analyses to persistent local history
• Includes a Streamlit front end

The author states this was built solo using Codex end-to-end, with statistical logic kept separate from LLM layer for verification.

The description states the author's positioning is:

• To stand in for a statistician who catches common errors before decisions are made
• Not an LLM guessing at significance but a tool that actually runs correct tests
• To focus on trustworthiness of results, not dashboards or p-values out of context
• To recommend shipping only when results earn it

The claim evolution appears to be from a personal interview preparation tool to a general-purpose A/B testing assistant. The author states they're a Data Scientist interviewing for senior roles and the pitch they keep making is that experimentation rigor is the actual job, not dashboards.

The description states:

• The target customer is teams that run A/B tests but don't have dedicated statisticians
• The tool addresses a failure mode where someone sees a green metric in a dashboard and ships without checking assumptions
• The author's stated motivation is to replace the statistician role on teams that lack one

No specific ICP or persona details are provided beyond this general description of teams lacking statistical expertise.

Not evidenced. The description does not contain any information about pricing, monetization, or business model.

The description states:

• Built solo using Codex end-to-end
• Statistical logic deliberately kept free of API calls for unit testing and hand verification
• Uses Codex to write scaffolding, CLI entrypoint, synthetic data generator, statistical core
• Every extension was a scoped Codex prompt reviewed before acceptance
• Statistical core separated from LLM layer for verification
• Includes Streamlit front end so no terminal access required
• Uses standard Wald interval for binary case confidence intervals

Not evidenced. The description contains no information about revenue, customers, usage metrics, or adoption.

Not evidenced. The description does not contain any information about competitors or market positioning beyond the author's own claims.

The description states:

• Risk of getting the math wrong, which is described as "the one thing this project can't afford to get quietly wrong"
• Risk of trusting Codex's statistics before building anything on top
• The tool was built in a hackathon context with limited time and resources
• No evidence of independent verification or testing beyond synthetic scenarios
• The author notes that verifying Codex-generated statistics against synthetic ground truth is not optional busywork but actual engineering work

1. What specific validation has been done on the statistical accuracy of the implemented tests?
2. How was the tool tested against real-world data or edge cases beyond synthetic scenarios?
3. What are the actual assumptions and limitations of the statistical methods used?
4. Has the tool been tested for bias in its recommendations or narrative generation?
5. What is the plan for handling multiple simultaneous metrics with multiple-comparisons correction?
6. How does the tool handle sequential testing and peeking detection?
7. What evidence exists that teams would actually use this instead of existing dashboards?