Bar and line graphs are routinely used to present continuous data in small sample size studies. This is problematic because many different datasets can lead to the same bar or line graph and the actual data may suggest different conclusions from the summary statistics. Many journals are changing their policies to require more transparent figures that allow readers to critically evaluate published data (dotplots, boxplots, etc.).

This course teaches learners how to select the right type of figures when presenting continuous data in small sample size studies. Learners are referred to several free resources which will allow them to quickly make static and interactive graphics. Participants will also learn how to use a free, web-based tool to create interactive line graphs for scientific publications.

Free Course Content:

All videos can be viewed free of charge. Payment is only required if CME credit is desired.

The Problem with Bar and Line Graphs (Watch Video)

Changing Standard Data Presentation Practices (Watch Video)

Line Graphs: Interpretation & Limitations (Watch Video)

Creating Interactive Line Graphs: Interactive line graphs allow one to view different summary statistics, view responses from any individual in the dataset, focus on specific conditions, time points or groups where important changes are occurring and view change scores for any two time points or conditions.  (Watch Video)

Alternatives to Static Line Graphs (Watch Video)

Resources

• Beyond bar and line graphs: This short paper illustrates why it’s important to show the distribution of continuous data, instead of relying on bar and line graphs that only show summary statistics. Supplemental files include Excel templates for making univariate scatterplots for paired data, as well as and instructions for making these graphs in GraphPad PRISMWeissgerber T, Milic N, Winham S, Garovic VD. Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 2015;13(4):e1002128.http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128
• Creating interactive line graphs for small datasets: Includes the link for the free web-based tool. The supplemental file outlines alternatives to the static line graph that are useful for certain types of datasets. Weissgerber TL, Garovic VD, Savic M, Winham SJ, Milic NM. From Static to Interactive: Transforming Data Visualization to Improve Transparency. PLoS Biol 2016;14(6):e1002484.http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002484
• How to present your data in scientific talks, from David Stern: http://www.howtogiveatalk.com/principle-3---show-your-data.html
• Blog post showing why you should look at your data before you compute summary statistics, from Till Bergmann: http://tillbergmann.com/blog/articles/barplots-are-pies.html
• Data presentation matters: a partial solution to the reproducibility crisis http://mikethemadbiologist.com/2015/04/28/data-presentation-matters-a-partial-solution-to-the-reproducibility-crisis/
• Strategies for making effective figures: Edward R.Tufte (2001) [1983]. The Visual Display of Quantitative Information (2nd Edition ed.). Cheshire, CT, USA: Graphics Press. ISBN 0-9613921-4-2.
• Lasagna plots: Swihart BJ, Caffo B, James BD, Strand M, Schwartz BS, Punjabi NM. Lasagna plots: a saucy alternative to spaghetti plots. Epidemiology 2010;21(5):621-5.http://journals.lww.com/epidem/Fulltext/2010/09000/Lasagna_Plots__A_Saucy_Alternative_to_Spaghetti.15.aspx
• Deconstructing the spaghetti plot by emphasizing one line at a time: Knaflic CN. Strategies for avoiding the spaghetti graph, Storytelling with Data, March 14, 2013, http://www.storytellingwithdata.com/blog/2013/03/avoiding-spaghetti-graph.
• Show all data points; then use lines to display individual responses for a few participants: Diggle PJ, Heagerty PJ, Liang KY, Zeger SL (2002) Analysis of Longitudinal Data. Oxford: Clarendon Press.
• Using trend lines to show changes from baseline: Bella On a Par, Bella Consultants, March 30, 2012, http://www.bella-consults.com/page/4.

ACCREDITATION:
Mayo Clinic College of Medicine is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.  

Mayo Clinic College of Medicine designates this enduring material for a maximum of .5 AMA PRA Category 1 Credits™.  Physicians should claim only the credit commensurate with the extent of their participation in the activity.