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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Korean journal of anesthesiology Jg. 70; H. 4; S. 407 - 411
Hauptverfasser: Kwak, Sang Kyu, Kim, Jong Hae
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Korea (South) The Korean Society of Anesthesiologists 01.08.2017
Korean Society of Anesthesiologists
대한마취통증의학회
Schlagworte:
bias
data collection
data interpretation
Statistical Round
statistics
마취과학
Data collection
Statistics
Bias
Data interpretation
ISSN:2005-6419, 2005-7563
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:2005-6419
2005-7563
DOI:10.4097/kjae.2017.70.4.407