Experimental Design and Analysis

Identify and mitigate experimental biases including selection, performance, detection, and attrition bias through rigorous blinding and allocation concealment strategies.

Identify, map, and control confounding variables in experimental and observational research using randomization, stratification, matching, and statistical adjustment strategies.

Model and interpret dose-response relationships using sigmoidal, log-linear, and hormetic models for pharmacology, toxicology, and environmental research.

Plan, analyze, and interpret factorial experiments including full and fractional factorial designs, interaction effects, and ANOVA for multi-factor studies.

Validate scientific measurement instruments using reliability analysis, confirmatory factor analysis, convergent validity, and item response theory for research and clinical scales.

Design feasibility and pilot studies to test protocols, estimate parameters, and inform the design of larger definitive trials before full-scale research begins.

Calculate required sample sizes and statistical power for any study design, including t-tests, ANOVA, regression, and survival analysis, with assumption justification.

Design rigorous quasi-experiments using difference-in-differences, regression discontinuity, and interrupted time series when randomization is not feasible.

Design rigorous randomized controlled trials (RCTs) with proper randomization, blinding, and power calculations for clinical and behavioral research.

Design and analyze within-subjects, repeated measures, and crossover experiments with carryover control, sphericity testing, and mixed ANOVA models.