FAQ: Misleading and Confusing Graphs - Color Associations

This community-built FAQ covers the “Color Associations” exercise from the lesson “Misleading and Confusing Graphs”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Machine Learning/AI Engineering Foundations
Data Scientist: Analytics Specialist
Data Scientist: Natural Language Processing Specialist
Data Science Foundations
Data Scientist: Inference Specialist
Data Scientist: Machine Learning Specialist
Business Intelligence Data Analyst

Principles of Data Literacy

FAQs on the exercise Color Associations

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How would choosing traditional colors for associated variables be potentially harmful? Harmful is a powerful word. Would some people really get turned off by this? Wouldn’t it be to their own detriment since they are the ones who are trying to get ideas from the data viz? I’m not trying to be argumentative but I just don’t see how harm could potentially be caused.

For the example of choosing blue to represent men and pink to represent women, the association with those colors isn’t objective. Its true because of how these colors historically have implications they aren’t necessarily indicative. But I think since the line of work I hope to get into with this and the tech industry overall has works with the idea conveyed in the lesson, the only thing Left to do is learn the new idea and keep it in mind. I just think some people would think “why did they choose those colors?” It is a good idea to try to be as inclusive as possible and if color is going to be a bigger deal than it has to be, could more neutral colors work or is color really that much of a deal that it would confuse people if it were neutral?

Referring to the New York Times article from 2014 “Gender in De Blasio’s Cabinet”, wouldn’t it be more aligned with the “universal design” belief if the legend for men and women were reversed? I mean, why are men on the left and women on the right when in the actual Data Viz, women are on the left and men on the right? I’m not colourblind, but it still confused me. I imagine that coloublind people might associate the relative position (left-right) with the interpretation of data.

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“switching up colors that have existing cultural associations can reduce harmful stereotyping”.

This is an inherently political and worldview based statement. Can someone explain why y’all are inserting political propaganda in your data science courses?

I think it’s helpful to understand what role aesthetics play in data visualization. I like the highlighting of how people think and interpret information. The section about presenting to the different eco stakeholders is a prime example of meeting people where they are, not alienating them. It was also nice to see how data visualization challenged the norm. I think it makes you pay attention and critically think about the information you’re digesting. It may also lead to more questions where you can find evidence-based answers.