Data Visualisation and Misrepresentation

Overview

This lecture covers how data visualisation is used to make data easy to understand, but also how it can be manipulated to misrepresent facts.

Data visualisation translates complex data into visual formats like charts and graphs to reveal patterns and trends.
Visual aids make large sets of numbers easier to interpret and understand.

Cherry picking data is selecting only data that supports a desired outcome, giving a false impression.
In the veterinary industry, positive results are often selectively reported, especially in industry-funded studies.
Human drug trial results funded by industry are more likely to show positive outcomes than those funded by the government.

Using cumulative data instead of annual data can falsely show consistent growth by always increasing the graph.
Pie charts can mislead if survey participants can vote for more than one option, resulting in totals exceeding 100%.
Bar graphs can exaggerate trends by adjusting the y-axis scale to make small differences appear significant.

Data manipulation is common in politics, media, and industry to influence public perception in their favor.
Being aware of these techniques allows individuals to critically evaluate presented information and make informed decisions.

Data Visualisation — converting raw data into visual formats like charts or graphs for easier understanding.
Cherry picking — selecting only supportive data points, ignoring evidence that does not fit the desired narrative.
Cumulative Data — data that adds up over time, always making the graph look like it is rising.
Annual Data — data shown separately for each year, revealing year-to-year changes.
Y-axis Manipulation — altering the scale of the graph to exaggerate differences.

Review examples of misleading charts in news media.
Practice identifying potential misrepresentations in charts and graphs encountered in everyday life.