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Analysis of ted talk, Lies,
Damned Lies and Statistics by
Sebastian Wernicke
By Darpanraj Deoghare
Who is he?
Currently an Engagement
Manager at Oliver Wyman,
Sebastian Wernicke originally
studied bioinformatics at
Friedrich-Schiller-Universität Jena.
During his time in academia, he
devised an algorithm for analyzing
biological networks that now aids
researchers in dealing with their
innate complexity.
Insights
Insight – No.1
 Combination of 3 key
ingredients correlates with
the probability of success of
a TED talk
Insight – No 1 Contd
Topic
Components
of A Good
Ted Talk
Delivery
Visuals
used
Insight – No 2
 get-tedpad.com is the tool.
Browsing it gives information
about the best and worst TEDTalks
ever!!
Conclusions
Relevance No 1
Topic
 Sebastian first looked at the list of top
ten words that make a successful talk
and an unsuccessful talk.
 For example if you came to TED to talk
about how French coffee will spread
happiness in our brains than it will be a
successful talk. Whereas, if you wanted
to talk about your project involving
oxygen, girls, aircraft than the statistics
say the talk would not be successful.
Relevance No 2
Conducting Complex Analysis
Conduct & Study complex
analyses
 Identify the market trends
 Collect data on customer needs
 Develop plans to target the
demanding service
 Implement required business
process
Relevance No 2
Delivery
 Most Favourite talks = 50% + Least Favourite talks
 This is true for most of the cases
Summary
 Wernicke looked at a statistical model to see if the was a
way to make a really good, or a really bad Ted talk. What
three things did he look at?
 He gives the example of four things that are good to talk
about, and four things that are not so good. What are his
examples?
 Ted talks that are long are more, or less, popular than short
ones?
 At a conference in most disciplines it would never do to say
“etcetera, etcetera.” What does Wernicke say about that in
Ted talks?
 What does he say about appearance on stage?
 Although Wernicke uses humor in this talk, in the overall
message what is he implying about evaluating talks by
popularity?
Submitted for Internship
"Data Analytics " under
Professor Sameer Mathur ,
IIML
By, Darpanraj Deoghare
Thank You

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Analysis of ted talk, lies, damned lies and statistics by sebastian wernicke

  • 1. Analysis of ted talk, Lies, Damned Lies and Statistics by Sebastian Wernicke By Darpanraj Deoghare
  • 2. Who is he? Currently an Engagement Manager at Oliver Wyman, Sebastian Wernicke originally studied bioinformatics at Friedrich-Schiller-Universität Jena. During his time in academia, he devised an algorithm for analyzing biological networks that now aids researchers in dealing with their innate complexity.
  • 4. Insight – No.1  Combination of 3 key ingredients correlates with the probability of success of a TED talk
  • 5. Insight – No 1 Contd Topic Components of A Good Ted Talk Delivery Visuals used
  • 6. Insight – No 2  get-tedpad.com is the tool. Browsing it gives information about the best and worst TEDTalks ever!!
  • 8.
  • 9. Relevance No 1 Topic  Sebastian first looked at the list of top ten words that make a successful talk and an unsuccessful talk.  For example if you came to TED to talk about how French coffee will spread happiness in our brains than it will be a successful talk. Whereas, if you wanted to talk about your project involving oxygen, girls, aircraft than the statistics say the talk would not be successful.
  • 10. Relevance No 2 Conducting Complex Analysis Conduct & Study complex analyses  Identify the market trends  Collect data on customer needs  Develop plans to target the demanding service  Implement required business process
  • 11.
  • 12. Relevance No 2 Delivery  Most Favourite talks = 50% + Least Favourite talks  This is true for most of the cases
  • 13. Summary  Wernicke looked at a statistical model to see if the was a way to make a really good, or a really bad Ted talk. What three things did he look at?  He gives the example of four things that are good to talk about, and four things that are not so good. What are his examples?  Ted talks that are long are more, or less, popular than short ones?  At a conference in most disciplines it would never do to say “etcetera, etcetera.” What does Wernicke say about that in Ted talks?  What does he say about appearance on stage?  Although Wernicke uses humor in this talk, in the overall message what is he implying about evaluating talks by popularity?
  • 14. Submitted for Internship "Data Analytics " under Professor Sameer Mathur , IIML By, Darpanraj Deoghare