Visualisation & Underlined Psychology




We humans have been drawing insights since thousands of years ago, remember the art on the cave walls or the old beautiful handmade map you discovered in a museum? Visualization has always been around, just recently we have managed to link it with tools and technologies.

Psychology behind

Lie factor and why should we worry about it?

With the increasing complexity of graphs, we tend to forget the basic integrity principles. Lie factor is one such principle that gets subconsciously incorporated in our insights, resulting in false interpretation.

Theory behind Lie factor

Tufte introduces the “Lie Factor”, which is the ratio between the effect in the visualization and the effect in the data. So, if the effect in the data is 1, but in the visualization, it is 2, the lie factor would be 2/1 or 2. In order for the visualization to accurately represent the data, the Lie Factor should be as close to 1 as possible.

Interesting topics that I will be touching upon

  • Primal instincts of a human being
  • Lie factor (math and science behind)
  • Data evidence and established perceptions
  • Data communication and defining a direct relationship
  • A short case study

Drill down for the session (30 m)

  • Introduction to the topic (Primal instincts and visualization) - 5 m
  • Walkthrough the concepts of lie factor and examples in real-world - 10 m
  • Evidence and communication overview - 5 m
  • Case study - 7 m
  • Q&A (remaining time)


  • Just one, lot of enthusiasm

Speaker Info:

I am Sayantika, Data Engineer at Twilio and a student of Business Analytics at the Indian School of Business. For me, data and insights are like crafting a poem, simple yet powerful.

More about my journey -

Speaker Links:

I have contributed to various conferences a few of them are

  • Stackconf Berlin
  • PyCon US
  • CCDays Online by KonfHub
  • DevConf by RedHat
  • Infosec Girls Bangalore
  • PyLadies Bangalore

A short summary and open presentation can be found here - Link to my presentations

Youtube references for previously delivered talks


Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Beginner
Last Updated: