When AI-written analysis code runs without an error, it has proved that the code is valid and that your data fit its shape. It has not proved that the code estimates the quantity you meant to estimate. That takes separate evidence, and a research team using coding agents should keep that evidence next to the final table.
The checks below follow a validation strategy the Seascapemodels blog recently laid out for analyses written by coding agents. The author calls it a work in progress, and it reports no error rates, so treat it as practitioner guidance rather than a tested standard. The underlying ideas are older than AI: they are how careful analysts have always checked code they didn’t write themselves.
Before any code exists, write down in plain language what the analysis should estimate: the outcome, the comparison, the population, the unit of analysis, and every transformation and exclusion along the way. For example: “the difference in mean spring reading scores between intervention and comparison schools, among students enrolled in both fall and spring, adjusting for fall scores, with schools as clusters.”
That sentence is what everything else gets checked against. Without it, a reviewer (human or AI) can only ask whether the code looks reasonable, not whether it does what was intended. A specification also exposes logical problems early, when they cost a sentence to fix rather than a rerun.
Most silent errors live in data preparation, not in the model call. Joins are the classic case: one duplicated ID in a lookup table turns 200 participants into 201 rows, and nothing errors. Assert what you expect after every join, filter, and reshape, and stop the script when it doesn’t hold.
R
set.seed(2026)
survey <- data.frame(id = 1:200, score = rnorm(200))
sites <- data.frame(id = c(1:200, 17), # id 17 appears twice by mistake
site = sample(letters[1:5], 201, replace = TRUE))
joined <- merge(survey, sites, by = "id")
nrow(joined) # 201, not 200: no error raised
stopifnot(nrow(joined) == nrow(survey), # fail loudly instead
!anyDuplicated(joined$id))Python (pandas)
import numpy as np
import pandas as pd
rng = np.random.default_rng(2026)
survey = pd.DataFrame({"id": range(1, 201), "score": rng.normal(size=200)})
sites = pd.DataFrame({"id": list(range(1, 201)) + [17], # id 17 duplicated
"site": rng.choice(list("abcde"), 201)})
print(len(survey.merge(sites, on="id"))) # 201, no error raised
survey.merge(sites, on="id", validate="one_to_one") # raises MergeErrorFor larger pipelines, the R packages pointblank and validate let you declare these expectations as rules. Count checks have a limit, though: a join on the wrong key can produce exactly the right number of rows.
Turn your model specification around: simulate data from it with parameters you choose, run the simulated data through the same workflow, and see whether the parameters come back. If the code is supposed to estimate a slope of 0.5, it should recover about 0.5 on average across many simulated datasets. A stronger version checks coverage: 95% intervals should contain the true value in roughly 95% of simulations.
R
set.seed(2026)
fit_slope <- function(d) coef(lm(y ~ x, data = d))[["x"]] # the analysis under test
true_b <- 0.5
est <- replicate(500, {
d <- data.frame(x = rnorm(300))
d$y <- 1 + true_b * d$x + rnorm(300) # known data-generating model
fit_slope(d)
})
mean(est) # about 0.5Python
import numpy as np
import pandas as pd
rng = np.random.default_rng(2026)
def fit_slope(d): # same estimate as R's lm() slope
return np.polyfit(d["x"], d["y"], 1)[0]
true_b = 0.5
est = []
for _ in range(500):
x = rng.normal(size=300)
d = pd.DataFrame({"x": x, "y": 1 + true_b * x + rng.normal(size=300)})
est.append(fit_slope(d))
print(np.mean(est)) # about 0.5Run as written, these give an average of 0.504 in R and 0.501 in Python; the two languages draw different random numbers, so the values differ slightly. Two cautions apply. First, the test only covers the conditions you simulate, so simulate the ones that worry you: clustering, missing data, skewed outcomes. Second, write the simulation separately from the analysis code. The Seascapemodels post suggests building it in a separate agent session with memory turned off, so the same misunderstanding can’t shape both the generator and the analysis.
Each check answers a narrow question. Read passes and failures against your design, not as a certificate.
| Intended result | Independent check | What a pass cannot establish |
|---|---|---|
| The code estimates the stated quantity | Reviewer compares the written estimand with the code | That the estimand is the scientifically right one |
| Records merge correctly | Row and unique-ID counts after each join | That the right records matched; a wrong key can give the right count |
| The model recovers its parameters | Simulated data with known values | That your real data follow the simulated model |
| Results don’t depend on incidental choices | Row shuffles, covariate shuffles, second implementation | That shared assumptions are correct |
None of these checks can establish causal identification or the scientific truth of the model. They catch implementation errors: code that runs, produces plausible numbers, and answers a different question than the one you asked.
Pick one result your team plans to report and write down what independent check would have caught a plausible but wrong implementation. If the answer is “nothing we currently run,” that is the check to add first.
If you want a second set of eyes on how an AI-assisted analysis is specified and validated, DASS supports researchers with analysis planning and review.
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