The Bradford distribution (also called the Bradford Law of Scattering) is a right-skewed distribution with a peak at the distribution’s minimum. It is a special case of the beta distribution of the second kind or Pearson’s Type VI. It was first described by Bradford in 1949, when he used it to show how sources are distributed in the field of documentation. It shows how information about a particular subject is scattered throughout various references, where the information is likely to be found. The information isn’t randomly scattered, but rather follows the characteristic pattern (Fidel, 2002).

Essentially, Bradford found that a small number of popular journals contained as many papers on a particular topic as a larger number of papers (n), which in turn contains as many papers as an even larger number of papers, n2. The exact numbers depend on the particular topic being studied. But let’s say you were studying how often articles on the topic of “bariatric surgery” showed up in journals. You might find that the top 10 journals had 20 articles on bariatric surgery in the last year. Another 50 less popular journals might also have 20 articles on that topic, while the remaining journals (say, 200) also have the same number of articles (20). Thus, articles of interest tend to be clustered towards a core group of journals.

## PDF for the Bradford Distribution

The probability density function for the Bradford distribution is:

Where:

• x is a proportion.

## References

Crestani, F. & Ruthven, I. (2005). Information Context: Nature, Impact, and Role: 5th International Conference on Conceptions of Library and Information Sciences, CoLIS 2005, Glasgow, UK, June 4-8, 2005 Proceedings. Springer Science and Business Media.
Dodge, Y. (2006). The Oxford Dictionary of Statistical Terms. Oxford University Press.
Fidel, R. (2002). CoLIS 4: Proceedings of the Fourth International Conference on Conceptions of Library and Information Science, Seattle, WA, USA, July 21-25, 2002.

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.