Skill UI

BGPT Paper Search queries the remote MCP server to retrieve structured experimental data from full-text studies, returning methods, results, sample sizes, quality scores, and conclusions for efficient literature reviews and evidence synthesis.

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.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

This technique guides users on how to construct highly specific search queries for academic papers. Instead of relying solely on broad topic keywords, users should prioritize combining concrete numerical identifiers—such as exact sample sizes, population percentages, concentration levels, or specific year ranges—with methodological terms and discipline keywords. Using these numerical anchors significantly increases the uniqueness and precision of the search results, helping to pinpoint the exact study required.

Structures prospective clinical study design: classify endpoints with surrogate flagging, estimate sample size and power for means, proportions and survival, and score phase-gate feasibility (GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO) with named owners. Estimates only, never clinical fact.

Perform a priori power analysis to determine required sample sizes for studies. Supports closed-form calculations for t-tests, ANOVA, and regression, plus Monte Carlo simulations for complex models like mixed models and survival analysis. Helps justify sample sizes for grants and protocols by calculating minimum detectable effects and power curves.

This skill guides the design of statistically rigorous A/B tests for optimizing product features, UI/UX changes, onboarding flows, and pricing models. It generates a complete test plan, including formulating hypotheses, calculating required sample sizes, estimating test duration, defining primary and guardrail metrics, and providing a detailed guide on interpreting results (ship, iterate, or reject).

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.

This guide provides rigorous methodology and theoretical frameworks for conducting heterogeneity analysis, particularly for academic manuscripts focused on rural Chinese economies. It outlines criteria for dimension selection (e.g., household endowment, regional environment, policy timing), ensuring theoretical justification, and setting minimum sample size requirements to avoid superficial findings and common pitfalls.

A comprehensive methodological guide designed for researchers writing economics and finance papers. It specifies the rigorous requirements for conducting heterogeneity analysis, emphasizing the need for theoretical justification, sufficient sample size, and alignment with the underlying economic or institutional mechanisms (especially within the Chinese finance and trade context).

A comprehensive guide for researchers in Developmental Psychology on meeting rigorous Open Science standards. It details best practices for data and materials sharing using Persistent Identifiers (DOIs), mandatory preregistration of hypotheses, sample-size justification, and adhering to ethical requirements when working with minors and vulnerable populations. It ensures compliance and reproducibility in modern scientific reporting.

A comprehensive planning tool for developmental psychology researchers designing studies that must support a claim of change. It systematically guides users through critical methodological issues, including addressing age vs. cohort confounds, planning for measurement invariance across ages, handling attrition, and properly justifying sample size for growth slopes. Essential for strengthening the pre-analysis plan of longitudinal and cross-sectional designs.