Liar, Liar, Charts on Fire

When we present data, we go through an arduous process of collecting, cleaning and extracting what we need. Yet when we skew data, we manipulate what we want others to see presenting an inaccurate representation of our data’s story. How do you know when data has been fudged?

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1. Connie Dang Customer Support Specialist/ Aspiring Data Analyst conniedang576@gmail.com Twitter: @dangconniedang
2. §Manipulating scale §Cherry picking §Jumping to conclusions @dangconniedang
3. Source: https://heapanalytics.com @dangconniedang
4. Source: https://heapanalytics.com @dangconniedang
5. Source: https://heapanalytics.com @dangconniedang
6. @dangconniedang Source: https://heapanalytics.com/blog/data-stories/how-to-lie-with-data-visualization
7. Source: https://towardsdatascience.com/ @dangconniedang
8. Source: http://www.skepticblog.org/ @dangconniedang
9. Source: http://tylervigen.com/ @dangconniedang Incorrect conclusion: You shouldn’t buy an iPhone because you if you do, you will likely die by falling down the stairs.
10. §Choose a fitting visualization for your data §Brevity in visualization §Placement of axes §Design for audience §Move them Source: http://digitaluncovered.com/5-best-practices-data-storytelling/ @dangconniedang
11. Connie Dang conniedang576@gmail.com LinkedIn: https://www.linkedin.com/in/dangconnie/ Twitter: @dangconniedang