Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
    ai for stock trading
    Can Data Analytics Help Investors Outperform Warren Buffett
    9 Min Read
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Data Visualization Doesn’t Need to be Biased
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > R Programming Language > Data Visualization Doesn’t Need to be Biased
R Programming Language

Data Visualization Doesn’t Need to be Biased

DavidMSmith
DavidMSmith
4 Min Read
SHARE

At the FlowingData blog, data visualization commentator and Visualize This author Nathan Yau lists 5 misconceptions about visualization:

At the FlowingData blog, data visualization commentator and Visualize This author Nathan Yau lists 5 misconceptions about visualization:

  • Software does everything (Nathan notes “Personally, I use a lot of R and have a lot of fun in Illustrator”, but uses a lot of other tools as well.)
  • Visualization is for making data flashy
  • The more information in a single graphic, the better
  • It has to be exact
  • Visualization is too biased to be useful

I agree completely with Nathan’s comments on the last point above:

More Read

R Integrated Throughout the Enterprise Analytics Stack
A bit of fun with R
Choosing the Right Programming Language for A Corporate Database
Big Data Statistics in the Search for a Cure for MS
Poll: R Is the Top Language for Data Science 3 Years Running

There’s a certain amount of subjectivity that goes into any visualization as you choose what data to show and how to show it. By focusing on one part of the data, you might inadvertently obscure another. However, if you’re careful, get to know the data that you’re dealing with, and stay true to what’s there, then it should be easier to overcome bias.

After all, statistics is somewhat subjective, too. You choose what you analyze, what methods to use, and pick what to point out in reports.

News organizations, for example, have to do this all the time. They get a dataset, decide what story they want to tell (or find what story the data has to tell). Browse through graphics by The New York Times, and you can see how you can add a layer of information that objectively describes what the data is about.

This stands in contrast to the presentation I saw today at the Strata conference from Alex Lundry, Chart Wars: The Political Power of Data Visualization. (You can see a shorter version of his talk online). It was an entertaining talk, but his main point was to encourage data visualization partitioners to actively insert a point of view into the presentation of data. For example, he encourages more charts like the one on the right below, rather than the one on the left.

Usefuljunk-costs Usefuljunk-monster
(Images from Nigel Holmes’ paper, Useful Junk? The Effects of Visual Embellishment on Comprehension and Memorability of Charts.)

Lundry’s take is that because the image on the right is more easily recalled by those who have seen it, it’s naturally better. I disagree. My objection to the chart on the right isn’t just that uses chartjunk, nor that the teeth are disporoportionately sized to the values, nor even that the X “axis” is slanted upwards to exaggerate the rise. My objection is the chart on the right is that it actively pushes an analysis upon the viewer. As Nathan notes, there’s always an element of bias in what data is selected to be presented, and the way it’s presented. But good charts merely present data, and leave the analysis (obvious though it may be) to the viewer. When a chart takes on the burden of analysis for the viewer, that’s when it strays from data visualization into propaganda.

FlowingData: 5 misconceptions about visualization 

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Hidden AI, a risk?
Hidden AI, Real Risk: A Governance Roadmap For Mid-Market Organizations
Artificial Intelligence Exclusive Infographic
unusual trading activity
Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
Analytics Exclusive Infographic
Ai agents
AI Agent Trends Shaping Data-Driven Businesses
Artificial Intelligence Exclusive Infographic
Why Businesses Are Using Data to Rethink Office Operations
Why Businesses Are Using Data to Rethink Office Operations
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

The R-Files: Call for Nominations

2 Min Read

Big Data Bytes: How Open Source is Changing Business

1 Min Read

R Script Tracks Bookies’ Favorites for the Next Pope

1 Min Read

The Fallacy of the Data Scientist Shortage

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?