Skill UI

A comprehensive guide for designing and presenting rigorous experiments for submissions to the Journal of Software (JOS). It guides users through setting clear Research Questions (RQ), selecting real-world datasets and baselines, applying advanced statistical testing (effect size, significance), conducting ablation studies, and addressing threats to validity (internal, external, conceptual). This ensures the evidence strongly supports the claims, meeting top-tier academic standards.

This skill guides authors in evaluating if their climate research—focusing on dynamics, variability, or change—meets the high scientific standards of the Journal of Climate. It covers scope fit, rigorous model evaluation, statistical significance testing (e.g., handling autocorrelation), and adherence to AMS house style, ensuring the contribution advances fundamental climate understanding.

A comprehensive guide for strengthening empirical causal claims for top-tier economics journals. This framework teaches how to move beyond simple OLS regressions by rigorously testing for causal identification (e.g., DID, IV, RDD) and, crucially, mapping the statistical estimate back to a meaningful economic parameter or mechanism. It emphasizes the dual requirement of credible identification and deep economic interpretability.

A comprehensive academic guide for validating survey instruments. This resource details required standards for reliability (Cronbach's α, Composite Reliability), validity (AVE, Fornell-Larcker, HTMT), and diagnosing common method bias (CMV) in self-report studies. It outlines best practices for cross-cultural adaptation, pre-testing, and advanced statistical reporting necessary for high-impact academic publications.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

This comprehensive guide outlines the robust battery of checks required for high-impact empirical finance papers. It covers alternative specifications, subsample analysis, outlier sensitivity, and crucial multiple-testing adjustments. Use this framework to structure your methodology, ensuring your central claims are statistically rigorous, reproducible, and capable of withstanding intense academic peer review and scrutiny.

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 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.

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 tool provides a statistically rigorous framework for analyzing a user's private LinkedIn post performance data. It moves beyond simple averages, utilizing median/MAD and permutation testing to determine if observed patterns are genuine or statistical noise. It guides users through three steps: descriptive analysis, pattern mining, and sizing future A/B experiments, ensuring hypotheses are testable and avoiding over-conclusion. Works only with your own exported data.