Skill UI

Comprehensive guidelines for conducting rigorous empirical validation in financial forecasting and econometrics manuscripts. This framework covers essential steps such as ensuring data provenance, performing robust out-of-sample testing (rolling windows), establishing multi-layered benchmark models, and conducting significance tests (e.g., DM/MCS). It helps authors meet the high academic standards required for top-tier journals.

A comprehensive guide for developing scientifically rigorous, APA-formatted tables and figures for quantitative communication research. This skill emphasizes that exhibits must be self-contained, displaying effect sizes and uncertainty (not just significance stars), and following strict reporting standards for maximum clarity for reviewers.

This guide details the rigorous review standards and criteria for developmental psychology manuscripts, covering masked peer review, developmental significance, methodological rigor (age/cohort effects, invariance, attrition), JARS reporting, and TOP transparency. Use this resource to stress-test your paper before submission, understand expected revisions, or interpret complex decision letters.

A comprehensive guide for empirical economists on building a robust argument beyond simple statistical significance. It details how to address alternative economic explanations, test for specification fragility, and establish external validity, helping manuscripts survive demanding academic referee scrutiny.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.

A comprehensive guide for authors assessing the suitability of environmental health, toxicology, or epidemiology manuscripts for Environmental Health Perspectives (EHP). It emphasizes the need for strong public-health significance, rigorous methodology, and clear policy relevance, guiding authors on framing, statistical rigor, and structure.

This guide details the best practices for creating high-impact exhibits for scholarly manuscripts, particularly in Entrepreneurship Theory and Practice (ETP). It covers structuring empirical results (using magnitude over significance stars), developing vital theoretical mechanism figures, and presenting qualitative data using structures like the Gioia model, all adhering to APA standards.

This guide provides strict guidelines for structuring and presenting empirical results (tables and figures) in financial management manuscripts. It emphasizes making the main coefficient and its practical economic magnitude legible at a glance, adhering to high-standard journal norms (e.g., Wiley/FMA style). It covers best practices for event-study plots, IV tables, and ensuring standard errors and economic significance are always visible.

A comprehensive guide detailing the ICASSP peer review pipeline, covering single-blind review mechanics, technical committee evaluation criteria, and the strategic importance of evidence, significance, and reproducibility in signal processing papers. Use this to craft submission strategies and decode decision letters.

A comprehensive guide for preparing high-impact tables and figures for IMF Economic Review (IMFER) manuscripts. It dictates best practices for communicating international macro results to a dual audience of econometric referees and policy makers. Key principles include transparent country coverage, replacing significance asterisks with confidence intervals, and expressing coefficients in policy-relevant units (e.g., % of GDP).

A comprehensive guide for designing and presenting rigorous experiments for top-tier computer science journals. It covers best practices for ensuring reproducibility, matching evidence to claims, selecting appropriate baselines, performing statistical significance testing, and handling modern challenges like LLM contamination and system scalability measurements. Essential for passing double-blind peer review.

This guide provides best practices for creating high-impact tables and figures (exhibits) for academic journals, especially in behavioral economics. It emphasizes visualizing the core behavioral comparison—such as treatment effects, dynamic changes over rounds, or underlying distributions—so the main finding is immediately visible before reading any text. Key practices include using confidence intervals over significance stars, ensuring grayscale readability, and providing self-contained captions.