Visualizing Data for the Masses

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Wells's 1903 argument

Physical science and advanced thinking require mathematical analysis skills
Soon, citizen competence will include the ability to compute, analyze averages,, and understand extremes.

Wilks's 1951 simplification

"Statistical thinking will be essential for citizenship as reading and writing" (Marriott, 2014).

Wilks's breakdown of statistical thinking according to Marriot (2014).

Six core concepts

Expectation and variance - understanding averages, maximums, and minimums.
Distribution - Recognizing patterns in data variation
Probability - Assessing the likelihood of events
Risk - Evaluating potential costs or dangers
Correlation - Identifying relationships between variables

Basically, both thinkers highlight the need for data literacy in a world increasingly driven by information and analysis.

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Marriot (2014) argues that the traditional definition of statistical thinking needs to be expanded to include three new concepts which are data, cognition, and visualization.

Data

Data is the lifeblood of statistics, but it's not explicitly included in the current definition. Marriot (2014) highlights the risk of big data and data science, suggesting that statisticians risk being left behind if they do not embrace data in all its forms. (Marriot, 2014)

Marriot (2014) states that adding data to the definition of statistical thinking will not solve the problem on its own, but it will send an important message that statisticians are the original data scientists and embrace data in all its forms.

Cognition🟦🟦🟦🟦🟦🟦

The human ability to think statistically is limited and Kahneman's book exposes cognitive errors made by people and statisticians according to Marriot (2014).

Dual system thinking - Marriot (2014) states that Kahneman proposes two thinking systems:

System 1- fast, intuitive, prone to biases
System 2 - slow, logical, effortful

Statistical thinking relies heavily on system 2
Despite our natural cognitive limitations, Marriot (2014) reminds us that Kahneman offers strategies to mitigate errors, encouraging the conscious engagement of System 2 in statistical reasoning, since System 1's instinctive responses can lead to erroneous judgments.

Visualization🟥🟥🟥🟥▶️

Statisticians excel at visualization tools like histograms, and box plots but at the same time struggle with effective communication through visuals.
Including visualization in the definition of statistical thinking emphasizes statisticians' ability to analyze and communicate data effectively.
Statisticians should embrace collaboration with other professionals like graphic designers and neuroscientists to keep up with evolving data trends and expertise.

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References

Marriott, N. (2014), The future of statistical thinking. Significance, 11: 78-80. https://doi.org/10.1111/j.1740-9713.2014.00787.x