Grouped circular data in biology advice for effectively implementing statistical procedures

• Published in: Behavioral ecology and sociobiology Vol. 74; no. 8; pp. 1 - 8
• Main Authors: Landler, Lukas, Ruxton, Graeme D., Malkemper, E. Pascal
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Science + Business Media 01.08.2020; Springer Berlin Heidelberg; Springer Nature B.V
• Subjects: Animal Ecology; Behavioral Sciences; Biological effects; Biomedical and Life Sciences; Circadian rhythms; Circularity; Data collection; Life Sciences; Measurement techniques; METHODS PAPERS; Statistics; Zoology; Rounding error; Rayleigh test; Hermans-Rasson test; Type I error; Gini test; Rao’s spacing test
• ISSN: 0340-5443; 1432-0762
• DOI: 10.1007/s00265-020-02881-6

The most common statistical procedure with a sample of circular data is to test the null hypothesis that points are spread uniformly around the circle without a preferred direction. An array of tests for this has been developed. However, these tests were designed for continuously distributed data, whereas often (e.g. due to limited precision of measurement techniques) collected data is aggregated into a set of discrete values (e.g. rounded to the nearest degree). This disparity can cause an uncontrolled increase in type I error rate, an effect that is particularly problematic for tests that are based on the distribution of arc lengths between adjacent points (such as the Rao spacing test). Here, we demonstrate that an easy-to-apply modification can correct this problem, and we recommend this modification when using any test, other than the Rayleigh test, of circular uniformity on aggregated data. We provide R functions for this modification for several commonly used tests. In addition, we tested the power of a recently proposed test, the Gini test. However, we concluded that it lacks sufficient increase in power to replace any of the tests already in common use. In conclusion, using any of the standard circular tests (except the Rayleigh test) without modifications on rounded/aggregated data, especially with larger sample sizes, will increase the proportion of false-positive results—but we demonstrate that a simple and general modification avoids this problem.