Skill UI

Detects real-time significant price movements and unusual volume spikes across cryptocurrency markets. It calculates a composite significance score by combining price change, volume ratio, and market cap. Use it to track top gainers, biggest losers, or identify sudden market pumps across thousands of assets, with filtering options for category and timeframe.

Automates guidance for statistical significance calculations in data analytics, offering triggered best practices, example implementations in SQL, visualization, and BI workflows, plus validation against common standards.

Query NCBI ClinVar for variant clinical significance, mixing gene/condition searches, API/FTP downloads, and VCF annotations to support genomic medicine interpretation workflows.

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

A comprehensive guide for authors preparing empirical research manuscripts (especially in accounting). It details best practices for structuring main tables (keeping them lean), transforming statistical coefficients into interpretable economic magnitudes, and properly visualizing dynamic effects using event study graphs. It emphasizes moving beyond simple significance stars to provide deep academic context.

This skill serves as a comprehensive guide for authors submitting manuscripts to highly selective pure mathematics journals like Acta Mathematica. It assesses the work's significance, intellectual fit, required level of rigor, structural integrity, and adherence to top-tier academic standards, ensuring the research is ready for the global mathematical research community.

This guide outlines best practices for authors presenting theoretical and empirical results for top-tier journals like the American Economic Journal: Microeconomics (AEJ: Micro). It covers structuring propositions, building transparent numerical examples, creating schematic figures, and formatting tables (including mandated use of standard errors over significance asterisks) to ensure maximum clarity and adherence to academic publishing standards.

This guide outlines how to structure a rigorous robustness program for economic policy manuscripts. It emphasizes defending the overall policy takeaway, rather than merely reporting coefficient significance. Users learn to address common econometric threats—such as specification changes, pre-trends, multiple testing, and selection on unobservables—using advanced techniques (e.g., Wild-cluster bootstrap, Oster bounds) to ensure the policy conclusion is stable and credible.

This guide details best practices for transforming raw econometric outputs (coefficients, significance stars) into self-contained, policy-message-carrying exhibits for top-tier economic journals (e.g., AEJ: Policy). It emphasizes replacing significance stars with standard errors (SEs) or confidence intervals (CIs), creating a 'headline exhibit' that communicates the policy effect in interpretable units, and structuring tables and figures for maximum clarity for non-academic policymakers.

This skill guides authors on condensing complex empirical research findings (tables and figures) to meet the strict formatting constraints of top academic journals like AER: Insights. It enforces a maximum of five one-page exhibits, teaches authors how to select the headline result, central diagnostics, and mechanism, and ensures adherence to specific house styles, such as reporting standard errors instead of significance asterisks, while managing word count budgets.

This guide provides a comprehensive framework for understanding the academic peer review process used at AISTATS, covering everything from OpenReview submission workflows and author-reviewer dynamics to advanced decision criteria (correctness, significance, empirical support). It helps researchers strategize their submissions and responses to maximize acceptance chances in statistics and machine learning.

This comprehensive guide provides best practices for designing and presenting scientific experiments for high-impact academic journals. It covers structuring research around clear Research Questions (RQs), selecting strong and fair baselines, ensuring statistical rigor (mean ± standard deviation, significance testing), performing detailed ablation studies, and preventing common issues like data leakage and evaluation pollution. It is essential for authors aiming to validate complex computational findings.