Univariate distributions

Univariate ditribution is the probability distribution of an individual variable considered in isolation, without considering any relationships or dependencies with other variables. Each variable’s univariate distribution provides insights into the frequency and variability of its values across the dataset.

Univariate empirical distribution comparison using histograms

Univariate empirical distribution comparison plots, are histogram-like plots that offer a visual representation of the observed frequency distribution of values for a single variable within a dataset. These histograms partition the variable’s value range into bins and display the frequency of observations falling within each bin.

Assessing synthetic data Quality through univariate distribution comparison

Univariate empirical distribution comparison plots play a crucial role in evaluating the quality of generated synthetic data. By comparing the histograms of real and synthetic data, users can assess how well the synthetic data replicates the marginal distribution of individual variables

Key aspects to consider when comparing synthetic to real univariate distributions include the shape, central tendency, variability, and presence of outliers or anomalies in the histograms. These comparisons enable users to verify whether the synthetic data accurately captures the distributional characteristics of the original dataset at the univariate level, ensuring its fidelity and suitability for downstream analysis