EPPS6356

This site is used for my Data Visualization Class (EPPS6356).

Ashleigh’s Data Guide A personalized checklist for handling messy data.

Assignment 1

Anscombe’s (1973) paper highlights how datasets with nearly identical summary statistics (mean, variance, correlation, regression line) can nonetheless reveal strikingly different patterns when graphed. The four models demonstrate reliance on numerical measures alone can obscure key features such as non-linearity, outliers, and unusual data structures. The main lesson is the essential role of visualization in statistical analysis. Visualization of the models or data can improve quantitative summaries. A practical solution is to incorporate exploratory data analysis (EDA) techniques, such as scatterplots, boxplots, and residual plots, early in research to detect anomalies before modeling. Additionally, modern extensions like interactive visualizations and diagnostic checks can further safeguard against misleading interpretations, ensuring that conclusions rest on both robust statistics and an accurate understanding of the underlying data.

This chart from a UNICEF–Gallup survey shows what young people (ages 15–24) in 21 countries believe is the most important factor in determining success. The side-by-side horizontal bar charts are helpful for seeing within-country differences, but the design makes cross-country comparisons harder than necessary. Because the categories are split into four separate panels, the viewer has to scan back and forth across columns rather than directly comparing values on a single axis. The sorting of countries by income per capita adds context, but introduces a visual bias. Readers may infer causal relationships between national wealth and values without the chart explicitly addressing them. A clearer alternative would be to combine responses into a grouped or stacked bar chart, aligning all categories for each country in one view. Small multiples with consistent scales would make it easier to compare how emphasis on education, hard work, or family wealth varies across regions. Simplifying the layout would improve readability and strengthen the chart’s ability to communicate cultural differences in beliefs about success.