Visualizing Data for the Masses
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
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.
Cognitionπ¦π¦π¦π¦π¦π¦
- 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.
Visualizationπ₯π₯π₯π₯▶️
- 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