Now that chat-based LLMs sit only a browser tab away, many people paste their experiment data into ChatGPT and ask, "Is this statistically significant?" The workflow feels easier than fishing around for an online A/B‑testing calculator, and the AI's lengthy answer can sound authoritative. Convenience and extra text, however, do not guarantee statistical rigor — or a reliable conclusion.
Before LLMs, most practitioners copied two numbers (sample size and outcome counts) into a significance calculator and accepted the p‑value at face value, unaware of hidden statistical defaults baked into the calculation. Swapping that calculator for an LLM doesn't remove these statistical blind spots. Without solid experiment design, you risk repeating the same mistakes and making equally unreliable decisions.
1. Assumption-Driven Conclusions
Some LLMs try to "fill in the blanks," guessing at standard deviations or equal variances when those details aren't supplied. Models such as ChatGPT-4o or Claude Sonnet often invent missing inputs, while Gemini 2.5 Flash or ChatGPT o4-mini tend to ask follow‑up questions. If the user lacks a solid experimentation framework, the conversation drifts and decisions end up resting on guessed numbers rather than real data.
2. Ambiguous or Underspecified Inputs
An LLM's guidance depends on the quality of the context it receives. Provide a column called duration without units or value without a definition, and the model must guess whether the metric is continuous (seconds per session) or binary (converted / not‑converted). Using a Welch's t-test on data that really needs a proportion z-test can turn an ordinary result into one that falsely looks statistically significant.
Experimentation teams at Netflix, Meta, Airbnb, and Booking.com share a planning‑first mindset. Netflix enforces MDE‑based power checks before any launch; Meta built internal tools like Deltoid to help teams understand how their features affect core metrics before rollout, reinforcing a culture of statistical rigor and impact awareness. Airbnb plans test duration in advance by calculating the minimum effect size that matters and factoring in daily sample volume, making sure each experiment is properly powered before it starts.
If experiment planning sounds new to you, you might be interested in our article How Long Should an A/B Test Run?. Solid up-front planning yields clean, well-scoped data for more reliable analysis—the same principle that underpins context engineering for LLMs, ensuring they generate higher-quality output.
LLMs aren't just a final check—they work best when woven into a thoughtfully planned A/B testing workflow. When you integrate them throughout, they can estimate your test's power or flag under-powered results:
Introducing our pilot tool, bridging structured test planning and intelligent LLM-powered analysis to continuously strengthen the rigor and precision of your A/B test cycles.
Curious to try it? Sign up now for early access and bring greater confidence to every test.