Data Science
Statistical Analysis & A/B Testing
2–6 weeksA/B testing & causalityDemo
Current versionNew version · winner
New version improvement
+14%
vs. current
Confidence
95%+
not luck (p<0.05)
People in the test
12.4k
enough sample (power 0.8)
Did your campaign actually work, or was it the market?Did this product change cause the retention increase, or was it coincidence?
We design and analyze experiments that separate real effects from statistical noise — and when a controlled experiment isn't possible, we apply causal methods that answer the hard questions just as rigorously. It's the difference between data and evidence. Technically: experiment design with power analysis and sample sizing, A/B testing with multiple-comparison corrections, sequential testing, and causal inference via difference-in-differences (DiD), synthetic control, regression discontinuity, and propensity score matching.
Use cases
- —You want to know whether a campaign truly created impact or simply coincided with the market
- —You need to design and read A/B tests without premature conclusions
- —You want to measure the real effect of pricing, policy, or product changes
- —You need to evaluate operational initiatives even when a perfect experiment is not possible
- —You want to turn business hypotheses into actionable statistical evidence
Deliverables
- —Experiment design with power analysis and sample sizing
- —Analysis report with effect sizes and confidence intervals
- —Significance analysis with multiple-comparison corrections
- —Causal models for observational settings (DiD, synthetic control, RDD)
- —Executive summary with decision recommendations