Weissgerber, Tracey

English

The Australian National University

Show the data instead of a bar graph for continuous dataAuthor Tracey Weissgerber (0000-0002-7490-2600), PhD, Mayo Clinic, QUEST, BIH Center for Transforming Biomedical ResearchFigure TypesExample Type of Outcome VariableWhat the Plot Shows Sample SizeData DistributionBest PracticesDot plot Continuous Individual data points & mean or median lineOther summary statistics (i.e. error bars) can be added for larger samplesVery small OR small; can also be useful with medium samplesSample size is too small to determine data distribution OR Any data distributionMake all data points visible - use symmetric jitteringMany groups: Increase white space between groups, emphasize summary statistics & de-emphasize pointsOnly add error bars if the sample size is large enough to avoid creating a false sense of certaintyDot plot with box plot or violin plotContinuous Combination of dot plot & box plot  or violin plot (see descriptions above and below)Medium Any Make all data points visible (symmetric jittering)Smaller n: Emphasize data points and de-emphasize box plot, delete box plot and show only median line for groups with very small nLarger n: Emphasize box plot and de-emphasize pointsBox plot Continuous Horizontal lines on box: 75th, 50th(median) and 25th percentileWhiskers: varies; often most extreme data points that are not outliersDots above or below whiskers: outliersLarge Do not use for bimodal dataList sample size below group name on x-axisSpecify what whiskers represent in legendViolin plot Continuous Gives an estimated outline of the data distribution. The precision of the outline increases with increasing sample size.Large Any List sample size below group name on x-axisBar graph Counts or proportionsBar height shows the value of the count or proportionN/A N/A Do not use for continuous dataTest p valueT-test: Equal var. 0.044 0.041 0.040 0.124T-test: Unequal var. 0.044 0.041 0.040 0.054Wilcoxon 0.053 0.065 0.014 0.177b c d eaSymmetricOutlierBimodalUnequal nWhy shouldn’t I use a bar graph for continuous data?Different datasets can lead to the same bar graph: The data may suggest different conclusions from the summary statistics.Can I use a bar graph even if my data are normally distributed?Not really, because: A: Bar graph (mean ± SE) B: Bar graph withdot plotC: Dot plotRange ofObservedValuesZone ofIrrelevanceZone ofInvisibility0Bar graphs1. Don’t allow you to critically evaluate continuous data2. Arbitrarily assign importance to bar height, rather than focusing on how the difference between means compares to the variability in the dataPlots to use for normally distributed, continuous data:…but bar graphs convey a clear message. Sometimes it’s hard to see what’s going on with dot plots.Step 1: Use symmetric jittering to make all points visibleStep 2: Emphasize summary statisticsEmphasize summary statistics & de-emphasize data points to convey a clear message while allowing readers to critically evaluate the dataDo I need expensive software?No, there are free, user-friendly tools:• Gnuplot• Veusz• Augl• Candela• Chartblocks• RCan I just add data points to my bar graph?Bar graph with dot plotDot plotWhy dot plots are better than bar graphs with points1. Shading obscures points2. Bars & vertical lines are chart junk. The solid shape creates the illusion of certainty without adding information.3. The “Zone of Irrelevance” falsely alters our perception of the size of the difference4. “Witin-the-bar bias”: We incorrectly believe that points are more likely to fall within the bar than above the bar1342We can easily see the magnitude of the difference & overlap between groupsFeatures that affect our interpretation (i.e. points, summary statistics, sample size) are clearly visibleZone of Irrelevance✗✔When is it appropriate to show summary statistics like error bars, box plots or violin plots?Summary statistics are only meaningful when there are enough data to summarize…With small samples, means and SDs can be very different from the true population values…Why shouldn’t I use box plots for bimodal data?Box plots conceal the two peaks. Bimodal distributions are easier to see with small (dot plots) or larger (violin plots) samplesHow do I make effective box or violin plots with dot plots?A: Box plotLimited information with small or medium samplesizesB: Box plot with unjittered dot plot (strip plot)Small improvement, but there are too manyoverlapping points to interpret the dataC: Emphasizing dot plotsSmall sample sizes: emphasize what is known (dots)& de-emphasize what is uncertain (box plots) Normotensiven = 50n = 10n = 15Gestationaldiabetesn  Preeclampsian = 15Subsequentpreeclampsian = 4Normotensiven = 50n = 10n = 15Gestationaldiabetesn  Preeclampsian = 15Subsequentpreeclampsian = 4Normotensive GestationaldiabetesPreeclampsia SubsequentpreeclampsiaNormotensive GestationaldiabetesPreeclampsia SubsequentpreeclampsiaD: Emphasizing box plots• Symmetric jittering• Box width proportional to n• n in x-axis label• No box plot for very small groupsFor the sample sizes shown in this figure, C is best. If all groups have larger samples, you can  choose C or D. These strategies also work for dot plots with violin plots.Ineffectivefor small ormedium nEffectivefor small n (C) ormedium n (C or D)100 random samplesn = 5 / sample100 random samplesn = 20 / samplePopulation mean ± SDAnalysis Strategy Example Figure Structure IllustrationComparing groups Figure compares wild type vs. knockout miceOne figure showing all groups that were included in the analysisRepeating the same analysis on different dependent (outcome) variablesFigure compares wild type vs. knock out mice. Three different tests are performed on different biomarkers.Separate panels for each analysis (i.e. dependent variable)Comparing groups with pooled subgroupsFigure compares wild type vs. knockout mice. Male and female mice are pooled.One figure showing all groups that were included in the analysis; data points for different subgroups are shown in different colorsStratified analysis Figure compares wild type vs. knockout mice. Separate analyses are performed for males and females.Separate panels for each analysisWhen possible, using the same scales can facilitate visual comparisonsTesting for an interactionFigure compares four different groups of mice (wild type / female, knockout / female, wild type / male, knockout / male). The analysis tests for an interaction between strain and sex.One figure showing all groups included in the analysisWT KOWT KOWT KOWT KOMaleFemaleWT KO WT KOFemale MaleBiomarker B Biomarker CWT KO WT KOWT KOBiomarker ASending Mixed Messages Figure structure erroneously suggests that authors also intended to compare biomarkers A, B and CClear communication Figure structure matches study design & analysis, shows that the authors did not intend to compare biomarkersExperimental goal: Compare wild type vs. knockout miceStatistical analysis: t-tests were used to compare values for each dependent variable  (biomarker A, B and C).Does it matter how I did my analysis? How do I design figures that match my study design & analysis?Yes! The figure structure gives the reader information about your design & analysis. Avoid sending mixed messages, especially with small datasets.For simple analyses of small datasets, it’s often clearest to show one graph per analysis that includes all groups, time points or conditions in the analysis.✗✔Figures are published in the following papers: DOI 10.1371/journal.pbio.1002128, DOI: 10/1074/jbc.RA117.000147 (add DOI for paper in review later) Biomarker A Biomarker B Biomarker CWT KO WT KOWT KOeLIFE Ambassadors 2019 CC-BY 4.0 International license.

Show the data instead of a bar graph for continuous data

https://openresearch-repository.anu.edu.au/bitstream/1885/155722/1/Data_Viz_Weissgerber v1.pdf

Show the data instead of a bar graph for continuous data

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The Australian National University