Can Generative AI Invent Functions or Package Names in Research Code?

What If the Statistical Function Recommended by AI Does Not Actually Exist?

You ask generative AI to write R code for a specialized statistical analysis. It recommends installing a package with a plausible name and calling a function that appears designed specifically for your research question.

The AI provides an explanation of the function, describes its arguments, and supplies an example of the expected output. Everything looks sufficiently technical to be credible.

However, when you try to install the package, R reports that it is unavailable. You search the package documentation and cannot find the function. When you ask AI for clarification, it confidently suggests another package or explains that the original function may have been renamed.

Was the function removed from a newer version, or did it never exist?

Generative AI can produce convincing programming instructions without verifying that the named functions, packages, or arguments are real. For researchers, this creates a problem that extends beyond wasted debugging time. An invented software reference can conceal an unsupported analytical method or lead to the installation of an untrusted dependency.

Yes, AI Can Fabricate Software References That Look Technically Credible

Why AI Can Invent Statistical Software References and How to Recognize Them

What Is a Hallucinated Function in Statistical Programming?

A hallucinated function is a function presented by an AI system as genuine when the claimed software interface does not actually provide it.

The generated name may resemble an existing function, combine terms from several packages, or describe a statistical procedure that the model assumes should have a convenient implementation.

For example, an AI system might invent a function named automatic_research_validity_test and claim that it evaluates whether a statistical model is appropriate for publication.

This name is a deliberately fictional illustration, not a verified function or package recommendation.

The problem is that statistical validity cannot generally be established by a universal function call. Even if such a function name existed in a custom project, its capabilities would need to be examined rather than accepted from its name.

Hallucination concerns the unsupported claim that a particular software reference exists or performs a specified operation.

Why Do AI Models Generate Plausible but Nonexistent Functions?

Generative AI produces programming text based on learned relationships among language, code, and technical documentation.

When asked for an unfamiliar or specialized procedure, the model may generate a function name that follows common naming conventions without confirming its existence.

For example, R functions often use descriptive names separated by periods or underscores, while Python libraries commonly use module paths and snake_case function names.

A fabricated reference can therefore resemble genuine code closely enough to escape immediate scrutiny.

The surrounding explanation may reinforce the illusion. AI can generate plausible arguments, example outputs, and installation instructions for a function that has not been verified.

The appearance of detailed documentation is not independent evidence that the software exists.

What Kinds of Software References Can AI Invent?

Hallucinations in research code are not limited to entire packages. They can occur at several levels of a programming interface.

Some errors cause immediate execution failures. Others may be harder to identify because the function exists but does not perform the operation described by AI.

How Is a Hallucinated Function Different From an Outdated Function?

An outdated function may have existed in an earlier version of a package. A hallucinated function, in the relevant sense, is one for which the claimed existence or behavior is unsupported.

Suppose a function is unavailable in the current environment. That observation alone does not establish hallucination.

The function might belong to a package that has not been loaded, have been renamed, or exist in a different software release.

It might also be a user-defined function supplied by another researcher.

Researchers should investigate the function's documented history before concluding that it was fabricated.

The separate issue of outdated statistical functions and packages requires version-specific verification.

Why Official Package Documentation Matters

Authoritative documentation provides information about the functions, arguments, outputs, and intended uses of statistical software.

For R, CRAN package pages and reference manuals can help verify packages distributed through that repository. Other legitimate R packages may be distributed through Bioconductor or maintained through project repositories.

For Python, the Python Package Index provides package-distribution information, while official project documentation explains supported interfaces.

For Stata, official documentation and trusted user-written command repositories can help establish whether a command exists and how it should be used.

For SAS and SPSS, the relevant product documentation provides command and procedure references.

Researchers should distinguish the existence of a package from the validity of its statistical methods. A genuine package can still be unsuitable for a particular analysis.

Can AI Invent Arguments for Real Statistical Functions?

Yes. A generated function call may reference a genuine function but include unsupported arguments.

For example, an AI system might invent an argument named validate_causality=True for an ordinary regression function, implying that the software can automatically establish causal validity.

This argument is a fictional illustration and should not be interpreted as part of a real regression interface.

The deeper problem is methodological. Ordinary regression does not become causally identified simply because a programmer adds a parameter with a reassuring name.

Researchers should inspect the function signature and determine what each accepted argument actually controls.

Even a real argument may be misrepresented by AI.

Why a Real Function Can Be Misdescribed

Not every AI coding hallucination involves nonexistent software.

A system may correctly name an existing function but incorrectly describe its statistical behavior.

For example, it might claim that a function performs a paired-samples t-test when the function actually compares independent samples.

The code could execute successfully and produce a result, making the error less obvious than a nonexistent function.

Researchers should therefore verify both the reference and its behavior.

This connects directly to the possibility that AI-generated code executes successfully while implementing the wrong statistical method.

Can Installing an Unfamiliar Package Create Security Risks?

Yes. Installing software from an untrusted source may introduce security, privacy, or integrity risks.

A fabricated package recommendation can lead researchers to search for similarly named packages without confirming their provenance.

Attackers may distribute malicious packages using names resembling legitimate software or exploit confusion about dependencies.

Researchers should not install an unfamiliar dependency merely because AI recommends it.

Verify the package's official distribution source, maintainer information, documentation, and suitability for the research environment.

Research datasets containing confidential information require additional safeguards before executing untrusted code.

Why Package Installation Success Is Not Proof of Legitimacy

A package may exist in a repository and install successfully without being the package the researcher intended to use.

Similarly, a package may provide the requested function name while implementing a different or inadequately documented statistical procedure.

Installation success therefore establishes only that software was obtained and installed under the environment's conditions.

Researchers should inspect the package documentation, verify the relevant methods, and test the implementation before relying on its output.

For specialized analyses, methodological references describing the estimator are particularly important.

How Can Researchers Verify a Function in R?

For a function claimed to belong to an R package, researchers can examine the package's documentation and exported objects.

For example, the following commands can help inspect a verified package that is already installed:

packageVersion("stats")
getNamespaceExports("stats")
help("lm", package = "stats")

The example uses R's standard stats package and the documented lm function.

These commands can help confirm version information, exported names, and function documentation.

However, not every legitimate function is exported publicly, and some functions are defined within a particular project rather than distributed through a package.

Researchers should interpret the findings according to the claimed source of the function.

How Can Researchers Verify a Function in Python?

Python provides introspection tools for examining imported modules and callable objects.

For example:

import inspect
from scipy import stats
print(hasattr(stats, "ttest_rel"))
print(inspect.signature(stats.ttest_rel))
print(stats.ttest_rel.__doc__)

This example checks whether the imported SciPy statistics module exposes ttest_rel, displays its callable signature, and retrieves its documentation string.

However, introspection does not establish that a function is statistically appropriate for a particular research question.

Nor does the presence of a callable guarantee that the imported package came from a trusted source.

Researchers should combine local inspection with authoritative documentation and provenance checks.

Can AI Hallucinate Statistical Procedures That Sound Scientifically Sophisticated?

Yes. A model may describe a nonexistent procedure using terminology associated with legitimate statistical methods.

For example, it might propose an automatic test that supposedly determines whether a regression coefficient is causal, whether all confounding has been eliminated, or whether a model is suitable for publication.

Such claims should be examined critically because statistical inference depends on assumptions and research design features that cannot ordinarily be validated through a universal software command.

The researcher should identify the actual estimator, its mathematical definition, assumptions, and methodological references.

A sophisticated name is not evidence of a scientifically established procedure.

Why Repeatedly Asking AI for a Replacement May Not Solve the Problem

When a generated function fails, researchers may ask the same AI system to correct the code.

The system may suggest another plausible but unverified function or explain the failure using an invented version history.

Repeated prompting can be useful for debugging, but it does not provide independent confirmation that the proposed software exists.

A more defensible approach is to consult the actual package documentation and then request code using verified functions.

When the required method is not available in the selected software environment, the researcher may need a different documented implementation or qualified statistical assistance.

When AI Invents a Convenient Function for a Statistical Analysis

The Fabricated AI-Generated Code

result <- stats::paired_research_significance(
pretest = data$pretest,
posttest = data$posttest,
confidence = 0.95
)
print(result)

The function name sounds relevant, and the arguments resemble those used in statistical programming.

However, the claimed function is not part of the documented R stats interface.

Attempting to execute the code in an ordinary R installation would fail because the requested exported function is unavailable.

How to Check the Claim

"paired_research_significance" %in%
getNamespaceExports("stats")
help("t.test", package = "stats")

The first expression checks whether the fictional function name is among the exported objects of the standard stats package.

The second command opens documentation for R's genuine t.test function.

Researchers should use official documentation to determine which functions are available and what statistical procedures they implement.

A Documented Alternative for the Intended Analysis

paired_data <- data[
complete.cases(data[, c("pretest", "posttest")]),
c("pretest", "posttest")
]
result <- t.test(
paired_data$posttest,
paired_data$pretest,
paired = TRUE,
conf.level = 0.95
)
print(result)
nrow(paired_data)

This implementation uses R's documented t.test function for a paired-samples comparison.

It selects complete pairs and reports the number of observations included.

However, the researcher must still verify that the scores are correctly matched, the test's assumptions are appropriate, and the resulting interpretation is consistent with the research design.

A paired-samples t-test can evaluate mean change but does not, by itself, establish that an intervention caused the change.

How the Researcher Should Respond

The example illustrates that fabricated software references can often be detected through documentation and interface inspection. However, replacing the invented function is only part of the task. The final analysis must still be statistically justified.

Common Misconceptions About Hallucinated Statistical Functions

How to Verify AI-Recommended Statistical Functions Before Using Them

Researchers should verify unfamiliar software references before incorporating them into an analytical workflow.

The verification should establish not only that a function exists, but also that it performs the required statistical procedure under appropriate assumptions.

Ask AI to Identify Verifiable Software References

A useful prompt is:

"Generate statistical code using documented functions from established packages. For every nonstandard function, identify the package, official documentation, required version, and relevant arguments. Distinguish verified software references from suggestions that have not been checked. Do not invent functions, package names, or analytical capabilities. If no suitable documented implementation is known, state that limitation rather than fabricating a command."

Such instructions may reduce unsupported references, but researchers should still check the cited documentation independently.

Do Not Install Packages Solely Because AI Recommends Them

Before installation, verify the source and inspect the package's documentation and requirements.

Use trusted repositories and institutional software policies where applicable.

For unfamiliar or potentially risky code, test the implementation in an appropriately controlled environment without exposing confidential research data.

Where the statistical method is specialized, consult methodological references or a qualified statistician before adopting an unfamiliar implementation.

Document the Verified Implementation

Retain the final script, relevant package versions, function documentation, and evidence that consequential calculations were tested.

When replacing a hallucinated or unsupported function, record the change and the reason for selecting the verified alternative.

The broader process of verifying AI-generated statistical code should include both software-reference checks and statistical validation.

Researchers remain accountable for the final analysis, regardless of whether the original software recommendation came from AI.

Before Trusting an AI-Recommended Statistical Function or Package

Frequently Asked Questions About Hallucinated Statistical Functions

A Convincing Function Name Is Not Evidence That the Function Exists

How to Cite This Guide

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