There are a number of TED talks that apply to research methods and statistics classes.
First, there is this TED playlist entitled The Dark Side of Data. This one may not be applicable to a basic stats class but does address broader ethical issues of big data, widespread data collection, and data mining. These videos are also a good way of conveying how data collection (and, by extension, statistics) are a routine and invisible part of everyday life.
This talk by Peter Donnelly discusses the use of statistics in court cases, and the importance of explaining statistics in a manner that laypeople can understand. I like this one as I teach my students how to create APA results sections for all of their statistical analyses. This video helps to explain WHY we need to learn to report statistics, not just perform statistics.
Hans Rosling has a number of talks (and he has been mentioned previously on this blog, but bears being mentioned again). He is a physician and conveys his passion for data-driven decisions regarding global public health issues via several talks. In addition to his talks, you and your students can play with his public health statistical visualization software at www.gapminder.org/.
This short video describes gold-standard research for the pharmaceutical industry but the research lessons apply across disciplines. It is also a nice way to introduce the reasoning behind independent and paired t-tests (the video emphasizes control vs. experimental research).
This video describes equal likelihood outcome probability and instances when outcomes are counter-intuitive. It includes probability calculations and touches on the fact that outcome frequencies that are dictated by mathematics only reveal themselves over MULTIPLE iterations of the event (for instance .5 probability of getting tails when you flip a coin).
For other TED and non-TED statistics videos, check out the video label for this blog.
UPDATE: 10/16/15
This talk by Dr. Alyson McGregor discusses how prescription drug trials typically use only male samples (due to fluctuations in female hormones over the a month). However, 80% of drugs that are recalled are recalled due to negative side effects in women. I think this is a good example for research methods, efficacy research, and sampling error.
UPDATE: 1/20/16
The TED-Ed talk illustrates Simpson's Paradox.
UPDATE: 2/3/16
Data-driven decisions! This video begins soft with examples of data-driven decisions made by Netflix in order to figure out what shows to develop, but gets real with examples of using data to make parole decisions and the Google Flu debacle.
UPDATE: 2/24/17
New playlist from TED, entitled "Statistically speaking..."
UPDATE: 10/30/18
Alan Smith's "Why you should love statistics." GREAT first-day-of-class video.
Update: 10/31/18
Steve Wang's 180 Degrees: A great talk about a more nuanced approach to REALLY understanding your data set.
First, there is this TED playlist entitled The Dark Side of Data. This one may not be applicable to a basic stats class but does address broader ethical issues of big data, widespread data collection, and data mining. These videos are also a good way of conveying how data collection (and, by extension, statistics) are a routine and invisible part of everyday life.
This talk by Peter Donnelly discusses the use of statistics in court cases, and the importance of explaining statistics in a manner that laypeople can understand. I like this one as I teach my students how to create APA results sections for all of their statistical analyses. This video helps to explain WHY we need to learn to report statistics, not just perform statistics.
Hans Rosling has a number of talks (and he has been mentioned previously on this blog, but bears being mentioned again). He is a physician and conveys his passion for data-driven decisions regarding global public health issues via several talks. In addition to his talks, you and your students can play with his public health statistical visualization software at www.gapminder.org/.
This short video describes gold-standard research for the pharmaceutical industry but the research lessons apply across disciplines. It is also a nice way to introduce the reasoning behind independent and paired t-tests (the video emphasizes control vs. experimental research).
This video describes equal likelihood outcome probability and instances when outcomes are counter-intuitive. It includes probability calculations and touches on the fact that outcome frequencies that are dictated by mathematics only reveal themselves over MULTIPLE iterations of the event (for instance .5 probability of getting tails when you flip a coin).
For other TED and non-TED statistics videos, check out the video label for this blog.
UPDATE: 10/16/15
This talk by Dr. Alyson McGregor discusses how prescription drug trials typically use only male samples (due to fluctuations in female hormones over the a month). However, 80% of drugs that are recalled are recalled due to negative side effects in women. I think this is a good example for research methods, efficacy research, and sampling error.
UPDATE: 1/20/16
The TED-Ed talk illustrates Simpson's Paradox.
UPDATE: 2/3/16
Data-driven decisions! This video begins soft with examples of data-driven decisions made by Netflix in order to figure out what shows to develop, but gets real with examples of using data to make parole decisions and the Google Flu debacle.
UPDATE: 2/24/17
New playlist from TED, entitled "Statistically speaking..."
UPDATE: 10/30/18
Alan Smith's "Why you should love statistics." GREAT first-day-of-class video.
Update: 10/31/18
Steve Wang's 180 Degrees: A great talk about a more nuanced approach to REALLY understanding your data set.
nice
ReplyDeleteVery relevant post on this topic.
ReplyDeleteThis is not a tough task to talk about statistical data analysis for a particular project. The best thing you can do is explaining statistics in a manner that laypeople can understand.
ReplyDeleteTo make the raw data easy and unproblematic to maneuver and construe, so that one can make improved and good judgment of the information, it is utterly imperative to systemize the entire compilation of data. See more non experimental research design
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