npcharts

NPcharts is a family of data visualization techniques designed to present non-parametric statistical information in a compact, interpretable form. Created to highlight distributional properties without assuming specific parametric models, NPcharts emphasize ranks, percentiles, and empirical distribution information to support comparisons across groups and conditions.

A typical NPchart displays multiple samples or conditions along a shared axis and uses non-parametric summaries such as percentile bands, medians, and rank-based indicators. The plots are often scale- and distribution-agnostic, relying on ranks or empirical cumulative distribution values rather than raw measurements. This approach aids in identifying differences in shape, location, and spread while remaining robust to outliers and skewed data.

Variants of NPcharts focus on different aspects of non-parametric visualization. NPchart-Quant emphasizes quantile-based comparisons, NPchart-CDF centers on empirical cumulative distribution representations, and NPchart-Probability integrates non-parametric test ideas to highlight pairwise or groupwise significance within the plotting framework.

Advantages of NPcharts include robustness to outliers, suitability for ordinal data, and ease of interpretation when distributions differ in shape rather than just central tendency. Limitations include potential loss of magnitude information and challenges in conveying precise effect sizes for large numbers of groups. Handling ties and small sample sizes requires careful preprocessing or explicit annotation within the chart.

NPcharts find use in exploratory data analysis, quality control, and biomedical research, especially when data violate normality assumptions. They can be implemented in statistical software through ranking-based plotting layers or custom routines built atop general plotting libraries, facilitating consistent, non-parametric visualization workflows.