Z-Dip: a standardized measure for data modality assessment

TL;DR

Z-Dip is a standardized, comparable measure for assessing multimodality in data distributions, overcoming limitations of existing methods like the Dip Test.

Contribution

The paper introduces Z-Dip, a universal, standardized modality measure that enables consistent comparison across datasets of varying sizes.

Findings

Z-Dip closely matches classical Dip Test decisions.

It provides a more interpretable and comparable modality measure.

Validation on over 88,000 empirical distributions demonstrates effectiveness.

Abstract

Detecting multimodality in empirical distributions is a fundamental problem in statistics and data analysis, with applications ranging from clustering to the study of complex systems. In practice, however, assessing departures from unimodality in a consistent and comparable way remains challenging. Widely used methods such as Hartigan and Hartigan's Dip Test illustrate these difficulties, as the interpretation of their statistics depends strongly on sample size, requires calibration to determine significance, and, for large samples, exhibit increasing sensitivity, leading to rejection of unimodality for arbitrarily small deviations from the null. We introduce Z-Dip, a standardized measure of multimodality that addresses these limitations. By treating the Dip statistic as a random variable under the null hypothesis of unimodality and standardizing its observed value, the proposed approach yields scores that are directly comparable across datasets of different sizes. Using simulation-based calibration, we derive a universal decision threshold that closely reproduces classical Dip Test decisions without requiring sample-size-specific adjustments. Extensive validation on simulated data and on more than 88,000 empirical opinion distributions shows near-perfect agreement with the classical Dip Test while providing a more interpretable and comparable measure of modality. Finally, we propose a downsampling-based correction that mitigates residual sensitivity in extremely large samples. Open-source software and reference tables are provided to facilitate practical adoption.