Skill UI

A comprehensive guide for executing and reporting complex social psychological analyses required for expert peer review. This skill covers norms for reporting uncertainty, assessing measurement quality, ensuring robustness checks, selecting appropriate statistical inference (e.g., multilevel models, clustering), and linking statistical findings back to the theoretical structure. It emphasizes methodological rigor and reproducibility.

A comprehensive guide for authors preparing manuscripts for Social Psychology Quarterly (SPQ). It ensures that all tables and figures are self-contained, highly accessible, and correctly formatted according to the ASA Style Guide. The focus is on clearly communicating the 'structure–individual link' and presenting complex statistical results to a mixed sociology/psychology readership.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.

A comprehensive guide for researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.

A comprehensive guide for conducting and reporting rigorous empirical analysis in academic research, particularly in finance and accounting. It covers selecting appropriate estimators (e.g., DiD, 2SLS, RDD), handling fixed effects, ensuring statistical robustness through placebo and alternative testing, and fulfilling strict data-authenticity and code-access requirements required by top-tier journals.

This skill details the rigorous process of converting raw statistical outputs into self-contained, publication-ready tables and figures for top-tier academic journals. It ensures compliance with strict house styles, covering sample selection, descriptive statistics, main regression results, and specific formatting for DiD, RDD, and event studies, all while adhering to strict page limits and providing economic magnitude interpretation.

This skill provides comprehensive guidance for submitting high-impact clinical and population research to The Lancet Respiratory Medicine. It details the journal's specialized scope, which favors practice-changing randomized trials and major prospective cohorts in respiratory and critical care. The guide highlights the rigorous evidence bar, including expected reporting guidelines (CONSORT, STROBE, PRISMA), statistical rigor, and structural house style, serving as a crucial fit and framing aid for authors.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

A specialized guide for authors aiming to submit to the Conference on Uncertainty in Artificial Intelligence (UAI). It provides expert advice on framing the core contribution—such as probabilistic reasoning, causal inference, and uncertainty modeling—and calibrating the manuscript for highly technical, statistical ML reviewers. Use this skill to refine your submission strategy, identify evidence gaps, and properly position your work relative to other top-tier AI venues like AISTATS and IJCAI.

A comprehensive audit checklist for designing and evaluating security experiments, adhering to the highest academic standards (e.g., USENIX Security). It guides users through verifying adaptive attacker models, calculating precision at realistic base rates, ensuring statistical honesty for stochastic results, and maintaining ethical compliance when working with live systems.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.

Provides comprehensive guidelines for designing and auditing academic tables and figures, specifically for development economics journals. It teaches how to translate statistical coefficients into actionable policy magnitudes, validate research design (e.g., balance checks, pre-trends), and adhere to strict page limits while maintaining clarity for both economists and policymakers.