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Presenting parasitological data: the good, the bad and the error bar

Published online by Cambridge University Press:  29 June 2015

SOPHIE G. ZALOUMIS ,
FREYA J. I. FOWKES ,
ALYSHA DE LIVERA  and
JULIE A. SIMPSON
SOPHIE G. ZALOUMIS*
Affiliation:
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Melbourne, Victoria 3010, Australia
FREYA J. I. FOWKES
Affiliation:
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Melbourne, Victoria 3010, Australia Macfarlane Burnet Institute of Medical Research, 85 Commercial Road, Melbourne, Victoria 3004, Australia Department of Epidemiology and Preventive Medicine and Department of Infectious Diseases, Monash University, 99 Commercial Road, Melbourne, Victoria 3004, Australia
ALYSHA DE LIVERA
Affiliation:
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Melbourne, Victoria 3010, Australia
JULIE A. SIMPSON
Affiliation:
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Melbourne, Victoria 3010, Australia
*
*Corresponding author. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Melbourne, Victoria 3010, Australia. E-mail: sophiez@unimelb.edu.au
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Summary

Visual displays of data in the parasitology literature are often presented in a way which is not very informative regarding the distribution of the data. An example being simple barcharts with half an error bar on top to display the distribution of parasitaemia and biomarkers of host immunity. Such displays obfuscate the shape of the data distribution through displaying too few statistical measures to explain the spread of all the data and selecting statistical measures which are influenced by skewness and outliers. We describe more informative, yet simple, visual representations of the data distribution commonly used in statistics and provide guidance with regards to the display of estimates of population parameters (e.g. population mean) and measures of precision (e.g. 95% confidence interval) for statistical inference. In this article we focus on visual displays for numerical data and demonstrate such displays using an example dataset consisting of total IgG titres in response to three Plasmodium blood antigens measured in pregnant women and parasitaemia measurements from the same study. This tutorial aims to highlight the importance of displaying the data distribution appropriately and the role such displays have in selecting statistics to summarize its distribution and perform statistical inference.

Keywords

Parasitological datadisplaygraphstatistical inferencedescriptive analysis
Type
Review Article
Information
Parasitology , Volume 142 , Issue 11 , September 2015 , pp. 1351 - 1363
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Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. and White, J. S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24, 127–135.CrossRefGoogle ScholarPubMed
Campbell, M. J. (2009). Statistics at Square One. [electronic resource], 11th Edn. John Wiley & Sons, Ltd., Chichester.Google Scholar
Campbell, M. J. and Gardner, M. J. (1988). Calculating confidence intervals for some non-parametric analyses. British Medical Journal (Clinical Research ed.) 296, 1454–1456.Google Scholar
Cumming, G., Fidler, F. and Vaux, D. L. (2007). Error bars in experimental biology. Journal of Cell Biology 177, 7–11.Google Scholar
Fowkes, F. J., McGready, R., Cross, N. J., Hommel, M., Simpson, J. A., Elliott, S. R., Richards, J. S., Lackovic, K., Viladpai-Nguen, J., Narum, D., Tsuboi, T., Anders, R. F., Nosten, F. and Beeson, J. G. (2012). New insights into acquisition, boosting, and longevity of immunity to malaria in pregnant women. Journal of Infectious Diseases 206, 1612–1621.CrossRefGoogle ScholarPubMed
Freeman, J. V., Walters, S. J. and Campbell, M. J. (2009). How to Display Data. Wiley, Hoboken.Google Scholar
Group, B. P. (1997). Statistics at Square One.Google Scholar
Hart, A. (2001). Mann–Whitney test is not just a test of medians: differences in spread can be important. BMJ (Clinical Research ed.) 323, 391–393.Google Scholar
Huff, D. (1993). How to Lie with Statistics. Norton, New York.Google Scholar
Kohler, U. and Kreuter, F. (2012). Data Analysis using Stata/Ulrich Kohler, Frauke Kreuter, 3rd Edn. Stata Press, College Station, Tex.Google Scholar
Matthews, J. N., Altman, D. G., Campbell, M. J. and Royston, P. (1990). Analysis of serial measurements in medical research. BMJ (Clinical Research ed.) 300, 230–235.Google Scholar
O'Hara, R. B. and Kotze, D. J. (2010). Do not log-transform count data. Methods in Ecology and Evolution 1, 118–122.Google Scholar
StataCorp (2013). Stata Statistical Software: Release 13. StataCorp LP, College Station, TX.Google Scholar
Vaux, D. L. (2008). Ten rules of thumb for the presentation and interpretation of data in scientific publications. Australian Biochemist 39, 37–39.Google Scholar
Wainer, H. (1984). How to display data badly. American Statistician 38, 137–147.Google Scholar
Weissgerber, T. L., Milic, N. M., Winham, S. J. and Garovic, V. D. (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biology 13, e1002128.Google Scholar
Zuur, A. F., Ieno, E. N. and Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution 1, 3–14.Google Scholar
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