IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006141.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006141
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006141&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1006141?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Ramal Moonesinghe & Muin J Khoury & A Cecile J W Janssens, 2007. "Most Published Research Findings Are False—But a Little Replication Goes a Long Way," PLOS Medicine, Public Library of Science, vol. 4(2), pages 1-4, February.
    3. Megan L Head & Luke Holman & Rob Lanfear & Andrew T Kahn & Michael D Jennions, 2015. "The Extent and Consequences of P-Hacking in Science," PLOS Biology, Public Library of Science, vol. 13(3), pages 1-15, March.
    4. Santiago Schnell, 2015. "Ten Simple Rules for a Computational Biologist’s Laboratory Notebook," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-5, September.
    5. Robert E Kass & Brian S Caffo & Marie Davidian & Xiao-Li Meng & Bin Yu & Nancy Reid, 2016. "Ten Simple Rules for Effective Statistical Practice," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-8, June.
    6. Steven Goodman & Sander Greenland, 2007. "Why Most Published Research Findings Are False: Problems in the Analysis," PLOS Medicine, Public Library of Science, vol. 4(4), pages 1-1, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stephan B Bruns & John P A Ioannidis, 2016. "p-Curve and p-Hacking in Observational Research," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    2. Martin E Héroux & Janet L Taylor & Simon C Gandevia, 2015. "The Use and Abuse of Transcranial Magnetic Stimulation to Modulate Corticospinal Excitability in Humans," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-10, December.
    3. Pierre J C Chuard & Milan Vrtílek & Megan L Head & Michael D Jennions, 2019. "Evidence that nonsignificant results are sometimes preferred: Reverse P-hacking or selective reporting?," PLOS Biology, Public Library of Science, vol. 17(1), pages 1-7, January.
    4. Adriano Koshiyama & Nick Firoozye, 2019. "Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical investigation of the ordinary and total least squares cases," Papers 1905.05023, arXiv.org.
    5. Bialek, Michal & Misiak, Michał & Dziekan, Martyna, 2021. "Why is the statistical revolution not progressing? Vicious cycle of the scientific reform," OSF Preprints gmfs9, Center for Open Science.
    6. Florian Englmaier & Andreas Roider & Uwe Sunde, 2017. "The Role of Communication of Performance Schemes: Evidence from a Field Experiment," Management Science, INFORMS, vol. 63(12), pages 4061-4080, December.
    7. Jeremy Arkes, 2020. "Teaching Graduate (and Undergraduate) Econometrics: Some Sensible Shifts to Improve Efficiency, Effectiveness, and Usefulness," Econometrics, MDPI, vol. 8(3), pages 1-23, September.
    8. Piotr Bialowolski & Dorota Weziak-Bialowolska & Eileen McNeely, 2021. "The Role of Financial Fragility and Financial Control for Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 1137-1157, June.
    9. Tracey L Weissgerber & Vesna D Garovic & Jelena S Milin-Lazovic & Stacey J Winham & Zoran Obradovic & Jerome P Trzeciakowski & Natasa M Milic, 2016. "Reinventing Biostatistics Education for Basic Scientists," PLOS Biology, Public Library of Science, vol. 14(4), pages 1-12, April.
    10. Jesper W. Schneider, 2015. "Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 411-432, January.
    11. Mayo, Deborah & Morey, Richard Donald, 2017. "A Poor Prognosis for the Diagnostic Screening Critique of Statistical Tests," OSF Preprints ps38b, Center for Open Science.
    12. Klaus E Meyer & Arjen Witteloostuijn & Sjoerd Beugelsdijk, 2017. "What’s in a p? Reassessing best practices for conducting and reporting hypothesis-testing research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 48(5), pages 535-551, July.
    13. David G Jenkins & Pedro F Quintana-Ascencio, 2020. "A solution to minimum sample size for regressions," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.
    14. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    15. Alexander Frankel & Maximilian Kasy, 2022. "Which Findings Should Be Published?," American Economic Journal: Microeconomics, American Economic Association, vol. 14(1), pages 1-38, February.
    16. Jyotirmoy Sarkar, 2018. "Will P†Value Triumph over Abuses and Attacks?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(4), pages 66-71, July.
    17. Stanley, T. D. & Doucouliagos, Chris, 2019. "Practical Significance, Meta-Analysis and the Credibility of Economics," IZA Discussion Papers 12458, Institute of Labor Economics (IZA).
    18. Karin Langenkamp & Bodo Rödel & Kerstin Taufenbach & Meike Weiland, 2018. "Open Access in Vocational Education and Training Research," Publications, MDPI, vol. 6(3), pages 1-12, July.
    19. Kevin J. Boyle & Mark Morrison & Darla Hatton MacDonald & Roderick Duncan & John Rose, 2016. "Investigating Internet and Mail Implementation of Stated-Preference Surveys While Controlling for Differences in Sample Frames," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(3), pages 401-419, July.
    20. Abel Brodeur, Nikolai M. Cook, Anthony Heyes, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," LCERPA Working Papers am0133, Laurier Centre for Economic Research and Policy Analysis.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1006141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.