Statistical data preparation: management of missing values and outliers

Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results. In addition, it causes a significant bias in the res...

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Bibliographic Details
Published in:Korean journal of anesthesiology Vol. 70; no. 4; pp. 407 - 411
Main Authors: Kwak, Sang Kyu, Kim, Jong Hae
Format: Journal Article
Language:English
Published: Korea (South) The Korean Society of Anesthesiologists 01.08.2017
Korean Society of Anesthesiologists
대한마취통증의학회
Subjects:
bias
data collection
data interpretation
Statistical Round
statistics
마취과학
Data collection
Statistics
Bias
Data interpretation
ISSN:2005-6419, 2005-7563
Online Access:Get full text
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Summary:Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results. In addition, it causes a significant bias in the results and degrades the efficiency of the data. Outliers significantly affect the process of estimating statistics ( , the average and standard deviation of a sample), resulting in overestimated or underestimated values. Therefore, the results of data analysis are considerably dependent on the ways in which the missing values and outliers are processed. In this regard, this review discusses the types of missing values, ways of identifying outliers, and dealing with the two.
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ISSN:2005-6419
2005-7563
DOI:10.4097/kjae.2017.70.4.407