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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Vydáno v:Korean journal of anesthesiology Ročník 70; číslo 4; s. 407 - 411
Hlavní autoři: Kwak, Sang Kyu, Kim, Jong Hae
Médium: Journal Article
Jazyk:angličtina
Vydáno: Korea (South) The Korean Society of Anesthesiologists 01.08.2017
Korean Society of Anesthesiologists
대한마취통증의학회
Témata:
bias
data collection
data interpretation
Statistical Round
statistics
마취과학
Data collection
Statistics
Bias
Data interpretation
ISSN:2005-6419, 2005-7563
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ObjectType-Review-3
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ISSN:2005-6419
2005-7563
DOI:10.4097/kjae.2017.70.4.407