Designed for inference
From lab to field, designs translate academic rigor into applied business experimentation.
Experimentation & Causal Inference
Evidence that moves strategy. PrimeStata designs, runs, and interprets experiments that isolate cause, quantify impact, and de-risk decisions.
Start an ExperimentEmpirical rigor meets business relevance
Statistical power, not guesswork
Power analysis, sampling strategy, and effect estimation ensure results that are both reliable and actionable.
Beyond correlation
Difference-in-Differences, instrumental variables, and matching frameworks for when randomization isn’t possible.
Human + AI analysis
Leverage machine learning and econometrics together for precision in insight and prediction.
From research to real-world return
Academic foundations
Grounded in validated psychometrics, measurement theory, and multilevel modeling—ensuring constructs truly measure what matters.
Consulting execution
Academic design becomes a business system: experiments that map to KPIs, programs that map to revenue, metrics that speak to leaders.
Hybrid fluency
The advantage: rigor of a PhD researcher with the agile execution of a consultant. Insight, action, and ROI stay connected.
Packages
Rapid A/B Framework
Lightweight experimentation setup for web, product, or HR interventions with a built-in analytics dashboard.
- Design and randomization logic
- Event tracking and effect estimation
- Real-time power and precision monitoring
Timeline: 2–4 weeks
Deploy A/B SystemBehavioral Experiment Lab
Controlled experiments leveraging psychological design, validated scales, and behavioral outcome measures.
- Stimulus design and validated psychometrics
- Survey platform integration (Qualtrics / custom JS)
- Factorial and within-subject designs
Timeline: 4–6 weeks
Run Behavioral StudyCausal Impact Evaluation
Program or policy evaluation using quasi-experimental designs—quantifying real-world impact where randomization isn’t feasible.
- Difference-in-Differences, IVs, Synthetic Control
- Longitudinal and multilevel modeling
- Executive readout and effect size interpretation
Timeline: 6–10 weeks
Request EvaluationMethods & tools
Experimental design
Between- and within-subject designs, blocked randomization, and multi-factor setups for precision inference.
Quasi-experimental analysis
Difference-in-Differences, fixed-effects models, and matching for observational or non-randomized data.
Multilevel & longitudinal modeling
Hierarchical and repeated-measures models for structures like teams, time-series, or nested organizational data.
Psychometrics & validity
Reliability (α/ω), EFA/CFA, measurement invariance—constructs that truly capture the behaviors and outcomes that matter.
Evidence of impact
R&D and HR experiments
Designed and analyzed multi-site field experiments linking behavioral data to retention and performance outcomes.
Marketing & product optimization
A/B frameworks that reduced churn and improved conversion by 10–20% in pilot studies.
Policy & program evaluation
Applied causal inference methods to measure program ROI, translating findings into actionable business and strategic decisions.
Ready to quantify what works?
Partner with PrimeStata to design, analyze, and interpret experiments that drive confident decisions.