With the increasing number of civil aircraft and routes, the civil aviation industry has entered the era of big data, and how to effectively detect abnormal data and avoid their adverse effect on the analytical results is the focus of many researchers. This paper focuses on the quartile method and the standard deviation method which is popular in civil aviation, including the distribution of data set, proportion of outliers, outlier detection capabilities, anti-disturbance and visual effects. Finally, the quick access recorder (QAR) data analysis demonstrates that the quartile method has better properties and is more suitable for application in the field of civil aviation warning compared with the standard deviation method.