Ten quick tips for getting the most scientific value out of numerical data

Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. Th
Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.
Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.
These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.
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Metadaten
Author:Lars Ole Schwen, Sabrina Rüschenbaum
URN:urn:nbn:de:hebis:30:3-476897
DOI:http://dx.doi.org/10.1371/journal.pcbi.1006141
ISSN:1553-7358
ISSN:1553-734X
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=30307934
Parent Title (English):PLoS Computational Biology
Publisher:Public Library of Science
Place of publication:San Francisco, Calif.
Contributor(s):Francis Ouellette
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/10/11
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/10/16
Tag:Data acquisition; Data processing; Data visualization; Enzyme kinetics; Monte Carlo method; Programming languages; Statistical data; Statistical methods
Volume:14
Issue:(10): e1006141
Pagenumber:21
First Page:1
Last Page:21
Note:
Copyright: © 2018 Schwen, Rueschenbaum. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HeBIS PPN:439212146
Institutes:Medizin
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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