SturgesRegel

SturgesRegel, also known as Sturges' Rule, is an empirical formula used in statistics to determine a suitable number of bins for a histogram. It was proposed by Allen W. Sturges in 1926. The formula is given by k = 1 + 3.322 log10(n), where k represents the number of bins and n is the total number of data points in the dataset.

The SturgesRegel aims to strike a balance between having too few bins, which can obscure the underlying distribution of the data, and having too many bins, which can lead to a sparse and noisy histogram that may not reveal the true shape of the data. The logarithm base 10 in the formula reflects the idea that as the number of data points increases, the number of bins should also increase, but at a decreasing rate.

While SturgesRegel is a widely recognized and often a good starting point for choosing the number of bins, it is not universally applicable. Its effectiveness can depend on the nature of the data, particularly its distribution. For datasets with non-normal distributions or those that are multimodal, SturgesRegel might not produce the optimal number of bins.

In practice, statisticians and data analysts often use SturgesRegel as a guideline rather than a strict rule. They may also consider other methods for determining bin width, such as Scott's rule or Freedman-Diaconis rule, and visually inspect the resulting histograms to assess which representation best captures the essential features of the data. The choice of the number of bins can significantly influence the interpretation of a histogram, so careful consideration is warranted.