R + AI
2026-03-279 min read

Why ChatGPT Produces Invalid R Code for Statistical Analysis

ChatGPT knows R syntax better than Stata. But it still produces statistically invalid code — wrong standard errors, missing diagnostics, and unvalidated inference.

Sytra Team
Research Engineering Team, Sytra AI

ChatGPT is better at R than Stata. It has more training data, the syntax is more Python-like, and R packages tend to have well-documented APIs. So you might think it’s safe to use ChatGPT for R-based statistical analysis.

It isn’t. The code runs, but the statistics are wrong.

The Syntax Is Fine. The Inference Is Not.

Here’s what ChatGPT generates when you ask for a DiD regression in R:

# ChatGPT’s DiD code
model <- lm(y ~ treated*post + controls, data = df)
summary(model)

This runs perfectly. The syntax is correct. But the inference is invalid because:

  • No clustered standard errors. lm() reports homoskedastic SEs. For panel DiD, you need cluster-robust SEs at the unit level.
  • No fixed effects. Two-way fixed effects require absorbing unit and time. ChatGPT uses lm() with dummy variables, which is computationally wasteful for large panels.
  • No staggered adoption check. If treatment adoption is staggered, vanilla TWFE is biased. ChatGPT doesn’t flag this.

What Correct R Code Looks Like

# Correct: using fixest for fast TWFE with clustering
library(fixest)
model <- feols(y ~ treated | unit + year,
data = df, cluster = ~unit)
summary(model)
 
# Staggered DiD: Callaway-Sant’Anna
library(did)
out <- att_gt(yname = "y", tname = "year",
idname = "unit", gname = "first_treat",
data = df)
ggdid(out)

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Test 2: Logistic Regression

ChatGPT generates:

model <- glm(y ~ x1 + x2, family = binomial, data = df)
summary(model)
→ Reports log-odds. No marginal effects.

The correct approach:

library(margins)
model <- glm(y ~ x1 + x2, family = binomial, data = df)
margins(model, type = "response")

Test 3: Survival Analysis

# ChatGPT’s version
library(survival)
model <- coxph(Surv(time, event) ~ treatment + age, data = df)
summary(model)
→ No PH test. No Schoenfeld residuals.
# The correct pipeline
model <- coxph(Surv(time, event) ~ treatment + age, data = df)
cox.zph(model) # PH assumption test
ggcoxzph(cox.zph(model)) # Visual check

The Pattern

Across all our tests, the pattern is consistent: ChatGPT generates syntactically valid R code that produces numbers. But the numbers lack the diagnostic context that determines whether those numbers mean anything. It’s like a medical test that gives you a result without telling you the sensitivity or specificity.

The fix isn’t “better prompting.” It’s building AI that understands inference — that knows lm() standard errors are wrong with clustered data, that Cox models require PH testing, that marginal effects are what clinicians actually need.

#R#ChatGPT#AI Coding#Biostatistics

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