Skill UI

Plan experiments before data is collected: choose a design, randomize and block units, avoid pseudoreplication, and generate reproducible layouts for factorial, screening, response-surface, crossover, split-plot, and cluster-randomized studies.

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

A comprehensive guide for planning and auditing highly rigorous biological experimental protocols, particularly for cancer mechanism studies. It emphasizes orthogonal validation across independent systems (in vitro, in vivo, human), detailing requirements for controls, replicates, sample size justification, randomization, and blinding to ensure scientific rigor.

A comprehensive methodological guide for auditing experimental economic designs. Use this when the causal claim hinges on strict experimental control, addressing issues like incentive compatibility, randomization robustness, and adherence to the no-deception norm. It stress-tests the underlying methodology, verifying identification through rigorous design checks.

Provides essential guidelines for designing rigorous, generalizable field experiments in crop research. Covers multi-environment trials (MET), proper randomization, blocking strategies, split-plot designs, and Genotype-by-Environment (G×E) analysis. Use this tool when planning or defending the scientific rigor of an agricultural manuscript.

This skill assists researchers in creating methodologically rigorous study designs for educational psychology manuscripts, especially when dealing with nested data structures (e.g., students within classes within schools). It guides users in matching the unit of randomization, power, and analysis to the correct cluster level. Focus areas include ensuring robust cluster-level power, establishing ecological validity, developing pre-analysis plans, and adhering to preregistration standards.

Provides a comprehensive checklist for ensuring methodological rigor in clinical and observational studies. It covers mandatory prospective trial registration (e.g., ClinicalTrials.gov), pre-specified protocols/SAP, and critical elements like randomization, blinding, and power calculation for RCTs. Essential for manuscript preparation and review.

This tool provides a comprehensive checklist and rigor check for writing clinical studies for top-tier journals like NEJM. It emphasizes the mandatory requirements for prospective trial registration (ICMJE), pre-specification of Statistical Analysis Plans (SAP), and adherence to design principles (e.g., randomization, blinding, ITT). It helps authors identify critical methodological gaps before submission.