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

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  • Lars Ole Schwen
  • Sabrina Rueschenbaum

Abstract

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.Author summary: Data expressed as numbers are ubiquitous in research in the life sciences and other fields. The typical scientific workflow using such numerical data consists of analyzing the raw data to obtain numerical results, followed by interpreting the results and presenting derived findings.

Suggested Citation

  • Lars Ole Schwen & Sabrina Rueschenbaum, 2018. "Ten quick tips for getting the most scientific value out of numerical data," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-21, October.
    • Handle: RePEc:plo:pcbi00:1006141
    DOI: 10.1371/journal.pcbi.1006141
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    References listed on IDEAS

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