Ethics of data representation

v2.0

Collect Raw Data Process and Filter Data Clean Dataset

Exploratory Analysis

Generate Conclusion

Generate Visualisation

Data Visualisation Process

What is Ethics when it comes to data visualisation?• The figure/graph/image should show what is actually

happening and not what you want to happen.

• Different ways of being unethical:– knowingly:

• deliberately showing the data in a misleading manner,• choosing the ‘most representative’ image/experiment.

– unknowingly:• not exploring/getting to know the data well enough,• misusing your chosen graphical representation.

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Cheating knowingly: Choice of graph

You know that what is going on

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• Hypothesis (what you want to see): Applying a treatment will decrease the levels of a variable.

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You choose to plot your data like that

Cheating knowingly: Choice of axis/scale

You know that what is going on

• You want to show an increase in salary in the last term.

You choose to plot your data like thatJune July Aug Sept Oct Nov Dec

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Cheating knowingly: Choice of axis/scale

• Be careful with Linear vs. logarithmic scale.

Cheating knowingly: Choice of axis/scale

• If you want to cheat, a bar graph using a log axis is a great tool, as it lets you either exaggerate differences between groups or minimize them.

Linear scale

Logarithmic scale

Cheating knowingly: Choice of axis/scale

• Logarithmic axis should be used for:

Lognormal data

Logarithmically spaced values

Original Brightness and Contrast

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Brightness and Contrast

Adjusted Too Much:

Oversaturation

Cheating knowingly: Manipulating images: Western blot

• Presenting bands out of context • ‘Playing’ too much with contrast

• ‘Rebuilding’ a Western blot from several cuts

Cheating unknowingly: Not exploring/getting to know the data well enough

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• Hypothesis: increase from CondA to CondB.You run the experiment once and you choose to plot the data as a bar chart.

Cheating unknowingly: Not exploring/getting to know the data well enough

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Comparisons: Treatments vs. Control

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Types of plotThings you can illustrate

Plot types – Distribution/ExplorationHistograms

• Very good for exploring data. Better on big dataset. • Rules: Number of intervals ≈√N and Interval width ≈ Range ÷√N• Histograms are great but careful with the resolution (= number of bins) as it affects the

shape of the distribution.

• Be careful with the resolution …

… and the type of data you are dealing with.

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Plot types – Distribution/ExplorationHistograms

• Histograms are great but careful with discrete data.

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Cutoff = Q1 – 1.5*IQR

Median

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Interquartile Range (IQR): 50% of the data

Lower Quartile (Q1) 25th percentile (1st quartile)

Outlier

Upper Quartile (Q3) 75th percentile (3rd quartile)

Plot types – Distribution/ExplorationBoxplots and Bean plots

Minimum

Plot types – Distribution/ExplorationBoxplots and Bean plots

Bimodal Uniform NormalDistributions

A bean= a ‘batch’ of data

Data density mirrored by the shape of the polygon

Scatterplot shows individual data

• Very good for exploring data. Better on medium size dataset. • Boxplots are great but be careful with underlying distribution.

Plot types – Exploration/ComparisonStripcharts/Scatterplots

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• Very good for exploring data. Better on small/medium dataset. • Very informative: exploration AND comparison.• Very hard to cheat with these. • Stripcharts are great but they don’t work so well with big samples.

Plot types – ComparisonsBarcharts

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Standard deviation Standard error

Confidence intervalStar wars (cool graph!)

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Plot types – ComparisonsBarcharts

• Be careful with the scale when plotting ratio

• Very good for presenting results and emphasizing differences.• Effectiveness: most important info with the most effective

channel.• Barcharts are great but after data exploration and the y-axis

needs to be chosen wisely.

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Plot types – Relationship/ComparisonLine graphs

Except for exploration …

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• Very good for presenting results of matched/paired/repeated data.• Linecharts are great but careful with the axes.

Plot types – RelationshipsScatterplot

• Very good for understanding the relationship between quantitativevariables.

Plot types – RelationshipsScatterplots

• Solution: smoothed densities colour representation

• Scatterplots are great but big data can be tricky.

Plot types – RelationshipsHeatmaps

• Great for big data sets, allow to plot a third quantitative value: colour scheme for grouping.

Euclidean distance Correlation Colour scheme

• Heatmaps are great but plot data that are changing.

A heatmap is basically a table that has colors in place of numbers.Simon’s data from simple numbers to correlation

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Plot types – CompositionStack charts/Pie charts

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• Stack /pie charts are great but keep an eye on the sample size.