Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
Showing 226–250 of 872 results
Not every sentence in an AI-generated response carries the same verification burden. Researchers should prioritize claims, citations, calculations, methods, interpretations, and other content whose accuracy affects the research.
Read Guide →Researchers remain responsible for factual claims they use, but that does not mean every AI-generated sentence requires identical verification effort. The sensible approach is risk-based: verify consequential claims rigorously and do not publish unsupported claims merely because AI supplied them.
Read Guide →A plausible scientific claim from AI is not evidence. Verify it by defining exactly what the claim asserts, locating appropriate scientific sources, and checking whether the evidence actually supports that specific conclusion.
Read Guide →An AI explanation of a statistical method can sound technically convincing while misstating an assumption, interpretation, or condition for use. Verify each important methodological claim against authoritative statistical sources and the requirements of your actual analysis.
Read Guide →AI-generated research code is not verified simply because it runs. Researchers should inspect what the code does, test it against known cases, check dependencies and assumptions, and confirm that it reproduces the intended analysis and results.
Read Guide →AI can produce correct calculations and convincing wrong ones. Researchers should verify consequential numerical results independently by checking the inputs, formula, operations, units, assumptions, and final interpretation.
Read Guide →An AI system can report research results accurately while interpreting them incorrectly. Verify its interpretation against the underlying data, statistical analysis, study design, uncertainty, and limits of inference before using it in a manuscript or conclusion.
Read Guide →An AI-generated table or figure can look polished while misrepresenting the underlying data. Verify every consequential value, calculation, label, scale, category, and visual encoding against the source data and analysis.
Read Guide →Asking AI to reconsider or check its answer can reveal some errors, but it does not constitute independent verification. Independent verification requires evidence or a checking process that does not depend solely on the original generative system.
Read Guide →A second AI model can catch errors the first model missed, but model agreement is not independent evidence that an answer is correct. Cross-model review is best used as an additional diagnostic layer before verification against authoritative sources, data, calculations, tests, or other external evidence.
Read Guide →AI models can sometimes detect and correct their own mistakes, particularly under structured verification procedures, but self-verification remains unreliable as a general guarantee of correctness. For consequential research work, use self-review to identify possible problems and external evidence to resolve them.
Read Guide →Independent verification means checking AI-assisted research against evidence or a validation process that does not simply depend on the generated answer itself. The appropriate method depends on whether you are verifying a claim, citation, calculation, code, result, or interpretation.
Read Guide →General-purpose and research-specific AI tools solve different problems. The better choice depends on the research task, the evidence you need, and how easily you can verify the output.
Read Guide →Paying for an AI tool can change access to models, features, usage limits, and data controls, but price does not establish research reliability. Compare what the specific plan changes for the task you need to perform.
Read Guide →A more capable AI model may perform better on difficult research tasks, but capability and reliability are not the same thing. Researchers still need task-specific testing, source verification, and appropriate safeguards.
Read Guide →One AI tool may be convenient across a research project, but convenience does not mean equal suitability. Different research tasks can require different sources, safeguards, verification methods, and capabilities.
Read Guide →Before putting an AI tool into a research workflow, test it on representative tasks, inspect consequential errors, verify its evidence and data practices, and decide what level of human checking remains necessary.
Read Guide →Widespread use by other researchers can help identify useful AI tools, but popularity is not validation. Your decision should depend on evidence about the tool, the task, and the risks in your own research.
Read Guide →An AI tool marketed for academics may offer genuinely useful research features, but academic branding is not evidence of accuracy or methodological validity. Evaluate what the tool actually does and what evidence supports its use.
Read Guide →Researchers do not need every proprietary detail of an AI system, but they need enough evidence to judge whether it is fit for a particular research use. That includes intended use, sources, evaluation, limitations, data practices, and important system changes.
Read Guide →Researchers should be able to understand the relevant information sources behind consequential AI outputs. Source transparency helps reveal what can be verified, what may be missing, and how much confidence an answer deserves.
Read Guide →Researchers should generally prefer verifiable citations when AI is used for source-dependent work. Citations make claims auditable, but only if the sources are real, identifiable, relevant, and actually support what the AI says.
Read Guide →Source citations make AI-generated answers easier to audit, but they do not guarantee correctness. Researchers must check that sources exist, support the attached claims, and justify the overall conclusion.
Read Guide →An AI tool’s terms of service can affect what researchers may upload, what rights apply to inputs and outputs, and who bears the risk when something goes wrong. Read the terms for the exact product and account you intend to use.
Read Guide →Institutional approval can be decisive when AI tools handle protected, confidential, or institutionally governed information. But approval establishes permission under particular conditions, not scientific validity for every research task.
Read Guide →