AI for biostatistics: an honest comparison
Three very different kinds of tools get lumped together when biotech teams talk about "using AI for stats." They fail in different places. Here is where each one actually fits, including where ours does not.
| Capability | General AI chat | Validated stats software | Quandra v8 |
|---|---|---|---|
Answers plain-language statistics questions Statistical packages need you to already know the procedure name. | — | ||
Every numeric claim re-derived and checked General chat models can state a confidently wrong number with no verification step. | — | ||
Citations to a fixed, versioned reference corpus Web-browsing models cite whatever they found today, which is not reproducible next quarter. | — | ||
Tamper-evident audit trail of every answer Required in practice for anything that touches a regulated decision. | — | ||
PHI is blocked before it reaches a model Pasting patient data into a consumer chatbot is a privacy incident, not a workflow. | — | ||
Generates runnable R and SAS code Generated code always needs human review before it touches a study dataset. | |||
Two-person sign-off before an answer is marked final A process control, not a model capability. | — | — | |
Formally validated for regulatory submission Quandra is research-use-only today. Validated statistical software remains the system of record. | — | — |
Quandra v8 is research use only. It is not validated software under 21 CFR Part 11 and it does not replace a qualified biostatistician. Every output is designed to be reviewed and signed by a human before it informs a regulated decision.
Where general AI chat breaks
General models are excellent at explaining a concept and terrible at being accountable for a number. They produce fluent, plausible statistics with no derivation you can inspect, no fixed source you can cite next year, and no record of what was asked. In a regulated environment, an unverifiable answer is worse than no answer, because it looks like work product.
Where validated statistical software breaks
Nothing beats it for the final analysis — that is why it is the system of record. But it assumes you already know which procedure to run, how to justify the assumptions, and how to write the section of the analysis plan that defends it. That knowledge gap is where small teams lose weeks.
What we built instead
Quandra sits in front of the validated software, not in place of it. Every numeric claim is re-derived by a deterministic solver, every factual claim is tied to a versioned reference document, and every answer carries an audit record and a human sign-off step. You still run the final analysis in your validated stack — you just get there with the reasoning already written down.
Check our math first
The deterministic core is published free. Use the calculators, then look at how the system scored against general models on 60 real biotech questions.