Skill UI

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

This comprehensive guide provides a rigorous framework for scientific reporting, ensuring statistical integrity and full reproducibility. It instructs authors on essential elements like reporting effect sizes, justifying sample sizes, controlling for multiple comparisons, and distinguishing biological from technical replicates, helping manuscripts meet the high standards of top-tier scientific journals.

This skill provides comprehensive guidelines for conducting and reporting high-stakes quantitative data analysis in the social sciences. It emphasizes methodological rigor, requiring honest reporting of uncertainty, probing robustness (specs that could break the result), and ensuring full reproducibility. It covers advanced topics like design-based inference, cluster corrections, and handling missing data, ensuring results withstand expert review.

A comprehensive guide for structuring research papers, particularly in computer graphics and visualization fields (like SIGGRAPH). It dictates how to match technical claims (e.g., 'higher quality,' 'faster') to verifiable evidence, including head-to-head comparisons against State-of-the-Art (SOTA), detailed metric reporting (PSNR, SSIM, Hausdorff), performance timing linked to specific hardware, and robust user studies. Essential for achieving academic rigor and pre-empting domain reviewer objections.

This comprehensive guide details the rigorous methodology for designing, executing, and reporting experimental results in Information Retrieval (IR). It covers best practices for selecting appropriate test collections, choosing defensible metrics, performing proper statistical significance testing (e.g., paired t-tests), ensuring baseline fairness, and proactively addressing modern pitfalls like LLM contamination. Essential for publishing high-quality, auditable research in IR/NLP.

A comprehensive guide for ensuring the reproducibility of Information Retrieval (IR) research, essential for SIGIR submissions. It details best practices for pinning data collection versions, index settings, neural model hyperparameters, and managing non-determinism. It helps researchers document artifacts—not just promises—to validate reported gains.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.

Provides comprehensive guidelines for achieving high levels of scientific reproducibility for academic papers, particularly those in systems metrics. It covers mapping claims to verifiable evidence, ensuring simulators are seeded and deterministic, and documenting measurement provenance to allow reviewers to independently verify all reported results.

This guide provides expert best practices for structuring and refining empirical tables and figures for top-tier academic journals. It teaches authors how to make exhibits self-contained, report complex econometric results (like DID and identification checks), and visualize nuanced mechanisms, ensuring maximum scholarly impact.

A comprehensive guide for designing rigorous Monte Carlo simulation studies, particularly for methods-heavy academic submissions (e.g., SMR). This guide covers specifying the Data Generating Process (DGP) space, selecting non-negotiable competitor methods, choosing appropriate performance metrics (e.g., power, bias, coverage), and critically reporting the limitations and failure regimes of the proposed method to ensure maximum credibility and reproducibility.

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 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.