RiceRegel

RiceRegel, also known as the Rice Rule, is a simple heuristic used in statistics to determine the number of bins in a histogram. The rule suggests choosing the number of bins as k = 2 × n^(1/3), where n is the number of observations. In practice, this yields a bin count that grows with sample size, but at a slower rate than many other rules.

Origin and naming are linked to historical work in data visualization, with the rule appearing in mid-20th century statistical literature. It is commonly presented as a straightforward, distribution-agnostic starting point for exploratory data analysis, especially when a quick visualization is needed without heavy computation.

Usage and applications: RiceRegel is widely used for initial histogram construction across disciplines, including statistics, data science, and research fields that rely on visual data inspection. It provides a rough balance between too few bins (which can obscure details) and too many bins (which can introduce noise).

Advantages and limitations: The main advantage is simplicity and speed; it requires only the sample size and no distributional assumptions. However, as a rule of thumb, it may not capture important features of the data, such as skewness, multimodality, or subtle tails. For datasets with unusual structure or when precise density estimation is required, other binning rules or adaptive methods may be preferable.

Relation to other rules: RiceRegel is one of several histogram binning rules, alongside Sturges', Scott's, and Freedman–Diaconis. Analysts often compare multiple rules or use kernel density estimation when a more refined view of the distribution is needed. See also histogram, binning, data visualization.