Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
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Hallucination rates can reveal factual reliability problems that overall accuracy scores may conceal. Learn how to define hallucinations, select an appropriate denominator, verify generated claims, and distinguish unsupported content from other AI errors.
Read Guide →An AI model may achieve high accuracy while producing inconsistent answers to identical questions. Learn how researchers can evaluate response stability, distinguish it from scientific reproducibility, measure variation across repeated trials, and determine when consistency matters for practical AI use.
Read Guide →High average AI accuracy can conceal poor performance for particular populations, languages, or task categories. Learn when subgroup analysis is necessary, how to measure performance differences, and how to interpret disparities without confusing statistical variation with evidence of discrimination.
Read Guide →An AI system may achieve high accuracy but require substantial time or expense to produce usable results. Learn how researchers can measure latency, throughput, token consumption, total workflow cost, and cost-effectiveness without confusing technical speed with practical efficiency.
Read Guide →An AI system may achieve high accuracy while requiring substantial human verification and correction. Learn how researchers can measure oversight requirements, distinguish necessary review from avoidable correction, and evaluate whether AI genuinely improves the quality and efficiency of research workflows.
Read Guide →Claims that AI performs better than humans require more than a higher benchmark score. Learn how researchers should define the human comparator, establish meaningful superiority, account for uncertainty, and distinguish task-specific performance from broader claims about professional competence.
Read Guide →A fair comparison between AI and human researchers requires more than asking both to complete the same questions. Learn how to select appropriate participants, control information access, define comparable tasks, reduce evaluator bias, and analyze performance without favoring either condition.
Read Guide →The choice of human comparator can fundamentally change the conclusion of an AI performance study. Learn how researchers should select relevant participants, define expertise, determine sample sizes, and establish human performance baselines that support meaningful comparisons rather than exaggerated superiority claims.
Read Guide →Comparing AI with experts and novices can produce very different conclusions. Learn when each group provides a fair human baseline, how expertise should be defined, and why studies may benefit from evaluating performance across multiple levels of human competence.
Read Guide →Restricting human participants from using their normal professional tools can make AI appear more capable than it would in realistic practice. Learn when tool restrictions are justified, how to distinguish unaided capability from workflow performance, and how to design fair human-AI comparisons without introducing avoidable methodological bias.
Read Guide →Giving AI access to retrieval systems, databases, and computational tools can substantially change its performance in human comparisons. Learn when tool access is justified, how to distinguish model capability from system-level performance, and how researchers can prevent unequal information access from producing misleading superiority claims.
Read Guide →Giving humans and AI identical information may strengthen some performance comparisons, but identical inputs do not always create equivalent conditions. Learn how researchers can establish information parity, distinguish controlled capability testing from realistic workflows, and prevent unequal access to evidence from distorting human-AI performance claims.
Read Guide →Equal time limits may appear fair in human-AI comparisons, but they can distort performance when humans and AI complete tasks differently. Learn when fixed deadlines are justified, how to measure task completion time, and why speed, accuracy, and human verification should be evaluated together.
Read Guide →Evaluators may judge identical responses differently depending on whether they believe the author is human or AI. Learn how expectations, source labels, response presentation, and perceived expertise can influence human-AI comparisons, and how researchers can measure and reduce these biases.
Read Guide →Two systems can achieve identical accuracy while making very different mistakes. Learn how researchers can compare AI and human error types, severity, overlap, and systematic failure patterns to assess reliability, complementarity, and the practical implications of human-AI performance differences.
Read Guide →AI and human experts may achieve similar average accuracy while differing in error patterns, reliability, judgment, and accountability. Learn how researchers can distinguish numerical similarity from statistical equivalence, evaluate interchangeability, and determine what evidence is needed before recommending AI as a substitute for human expertise.
Read Guide →Human-AI comparisons can produce impressive results while supporting much narrower conclusions than their headlines suggest. Learn how inappropriate human baselines, unequal information, selective reporting, flawed scoring, and unrealistic tasks can distort AI performance claims, and how researchers can design and interpret comparisons more responsibly.
Read Guide →When generative AI contributes to data collection, analysis, classification, or other research procedures, prompt design can influence the resulting evidence. Learn why prompting may constitute part of the research method, how prompts function as experimental conditions, and what researchers should document to support validity, transparency, and reproducibility.
Read Guide →AI prompt variants can influence research findings even when only one prompt appears in the final methodology. Learn which variants should be archived, how to preserve prompt histories and execution records, and how researchers can balance reproducibility, transparency, confidentiality, and practical documentation requirements.
Read Guide →Generative AI responses may depend on earlier conversation turns, instructions, examples, and retained context. Learn how conversation history can influence research outcomes, when fresh conversations are appropriate, and how researchers can document multi-turn AI interactions for valid and reproducible evaluation.
Read Guide →Starting each AI research test in a fresh conversation can reduce unwanted context carryover, but it is not always the appropriate experimental design. Learn when conversation isolation is necessary, how persistent memory and system instructions complicate testing, and how researchers can distinguish independent trials from realistic multi-turn interactions.
Read Guide →AI agents and chatbots may use the same underlying language models, but they differ in how they pursue goals, use tools, maintain state, and perform actions. Learn what makes an AI system agentic, why autonomy exists on a continuum, and how researchers should evaluate agentic systems without confusing conversational ability with independent action.
Read Guide →AI agents can autonomously execute selected research tasks, from retrieving literature to running analytical scripts. However, successful automation does not establish scientific validity or remove researcher accountability. Learn which tasks are suitable for delegation, how to evaluate agent reliability, and when human supervision remains necessary.
Read Guide →AI agents can connect literature retrieval, data analysis, and scientific writing into multi-step workflows, but completing every stage does not guarantee valid research. Learn how errors propagate across autonomous research pipelines, why independent verification matters, and when human intervention remains essential.
Read Guide →AI agents can clean, standardize, and transform research datasets, but unrestricted modification can compromise the scientific record. Learn which changes may be appropriate, why original data should be preserved, and how permissions, audit trails, validation, and human approval can distinguish legitimate processing from unacceptable manipulation.
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