The problem
Why this matters.
Some datasets are small by nature — rare diseases, oncology subgroups, niche populations, small clinical panels. Standard tools underperform or fail entirely when N is low, forcing teams to wait for more data or abandon the analysis.
Our approach
How we solve it.
Our mathematical framework was designed for small N from the ground up. It requires no distributional assumptions (non-parametric), preserves every data point's precision, and has been validated on real-world datasets where GWAS detected nothing.
Expected gains
Tangible outcomes.
Where traditional tools need thousands.
Analyse your own data without multi-site aggregation.
Publish and act on small-cohort findings.
Who uses this
Industries already applying it.
Let's discuss your challenge.
Tell us about your dataset, cohort size, or current bottleneck. We'll review and respond within a few working days.