Overview
Artificial intelligence, or AI, refers to computer systems designed to perform tasks that typically require human reasoning, pattern recognition, language understanding, prediction, or decision support. In research settings, AI tools can help organize information, explain concepts, suggest analytic strategies, generate code, summarize output, and support documentation.
StatWiseAI uses AI to support researchers as they plan and conduct data analyses. The goal is not to have AI “do the research” for the user. The goal is to help users ask better questions, consider appropriate methods, identify assumptions, and document decisions more clearly.
What is generative AI?
Generative AI refers to AI systems that can create new content, such as text, code, summaries, tables, explanations, outlines, or draft analytic plans. These systems can be useful because they respond conversationally and can adapt to the context provided by the user.
However, generative AI does not understand a research project the same way a trained researcher, statistician, or domain expert does. It produces responses based on patterns in data and the instructions it receives. This means that an AI-generated answer may sound confident even when it is incomplete, overly general, or wrong.
What is a large language model?
A large language model, or LLM, is a type of generative AI system that works with language. It can respond to questions, explain concepts, draft code, compare analytic approaches, and help users think through research decisions.
LLMs are especially useful when users provide clear context. For example, a vague question such as “What analysis should I run?” will usually produce a vague answer. A better question would describe the research question, study design, outcome variable, key predictors, dataset structure, and the type of help needed.
What AI can help with
StatWiseAI may be useful for tasks such as:
- Clarifying a research question.
- Understanding dataset documentation.
- Identifying relevant variables from a codebook or data dictionary.
- Comparing possible statistical approaches.
- Drafting an analysis plan.
- Generating code in R, Python, SAS, Stata, or SPSS.
- Reviewing statistical output.
- Suggesting sensitivity analyses.
- Identifying assumptions and limitations.
- Creating reproducibility checklists.
- Drafting documentation for analytic decisions.
What AI should not be used for
AI-generated output should not be treated as final expert guidance. Users should not rely on StatWiseAI to:
- Make final analytic decisions without human review.
- Guarantee that a statistical method is appropriate.
- Confirm that code is correct without testing.
- Determine whether a project complies with IRB, HIPAA, FERPA, or data-use requirements.
- Make causal claims from observational data without a defensible causal design.
- Interpret results beyond what the data and analysis support.
- Replace consultation with a statistician, methodologist, IRB, or data governance office.
Key principle
AI output is not evidence by itself. It is a draft, suggestion, or reasoning aid.
Before using AI-generated advice, researchers should check whether the response fits the research question, dataset, study design, variables, assumptions, and analytic goals. Any code should be reviewed and tested. Any interpretation should be compared with the actual statistical output and the relevant research literature.
Quick self-check
Before moving to the next section, consider:
- Do I understand what StatWiseAI can help with?
- Do I understand that AI-generated answers can be wrong?
- Do I know that I remain responsible for final research decisions?
- Do I know not to enter sensitive, proprietary, PHI, HIPAA-regulated, or FERPA-regulated information?
Start Here: Responsible Use Rules
AI Basics for Researchers
Prompting for Data Analysis
Working with Dataset Documentation
Reviewing AI Outputs
Requesting Code
Reproducibility and Prompt History
Practice Use Cases: HRS and NHANES
Templates and Checklists
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