Circular data analysis is a particular branch of statistics that sits somewhere between the analysis of linear
data and the analysis of spherical data. Circular data are used in many scientific fields. The efficiency
of the statistical methods that are applied depends on the accuracy of the data in the study. However,
circular data may have outliers that cannot be deleted. If this is the case, we have two ways to avoid the
effect of outliers. First, we can apply robust methods for statistical estimations. Second, we can adjust
the outliers using the other clean data points in the dataset. In this paper, we focus on adjusting outliers in
circular data using the circular distance between the circular data points and the circular mean direction.
The proposed procedure is tested by applying it to a simulation study and to real data sets. The results
show that the proposed procedure can adjust outliers according to the measures used in the paper.