Outlier detection using Grubb test and Cochran test in clinical data

그럽 및 코크란 검정을 이용한 임상자료의 이상치 판단

  • 손기철 (대구가톨릭대학교 의과대학) ;
  • 신임희 (대구가톨릭대학교 의과대학)
  • Received : 2012.05.10
  • Accepted : 2012.06.07
  • Published : 2012.07.31


There are very small values and/or very big values which get out of the normal range for survey data in various fields. The reasons of occurrence for outlier are two. One of them is the error in process of data input and the other is the strange response of the respondent. If the data has outliers, then the summary statistics such as the mean and the variance produce misleading information. Therefore, researcher should be careful in detecting the outlier in data. In particular, it is very important problem for clinical fields because the cost of experiment is very high. This article introduce the Grubb test and Cochran test to detect outliers in the data and we apply this method for clinical data.


Supported by : 보건복지부


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