Statistical Analysis & Experiment Design
Test Ideas the Right Way, from Design to Decision
Making changes to a product, website, or process without testing them properly can lead to misleading conclusions. Statistical analysis and experiment design — including A/B testing — helps organizations test ideas in a structured way, ensuring that results reflect real effects rather than chance. This can support product development, marketing, pricing, and operational decisions.
This service can be applied to a wide range of business questions, such as testing new website designs, pricing strategies, marketing messages, or process changes. By carefully designing experiments from the outset — including sample size, duration, and what to measure — businesses can be confident that the results they see are meaningful and not just noise.
The goal is not just to run a test, but to design it so that it answers the right question with a clear, statistically sound conclusion. Organizations can use these results to make informed decisions about which changes to roll out, avoid costly mistakes, and build a culture of evidence-based experimentation.
Case Study: Testing a New Checkout Design for an E-commerce Retailer
An e-commerce retailer wanted to reduce cart abandonment by simplifying their checkout process. However, redesigning the checkout page is a significant investment, and rolling it out to all customers without evidence that it actually improves conversion carries real risk.
To address this, an A/B test was designed to compare the new checkout flow against the existing one. This involved determining the sample size needed to detect a meaningful difference, defining the primary metric — conversion rate — and randomly assigning visitors to each version over a fixed period to avoid bias from seasonal or daily patterns.
The analysis showed a statistically significant increase in conversion rate for the new design, giving the retailer the confidence to roll it out to all customers. This approach reduced the risk of the investment, provided a clear estimate of the expected impact, and established a repeatable process for testing future changes.
Interested in designing experiments to test your next idea?
Get in touch to start testing with confidence.