« Tip #1 for reading economic stats: pull apart threads | Main | Some comments on football statistics, why it is hard »

Comments

Feed You can follow this conversation by subscribing to the comment feed for this post.

Dimitri

Interesting post, and I agree with the conclusion. However, this would not work if the information is presented with a drill-down functionality. Drilling down will effectively reduce the sample size and will magnify the influence of errors/typos.
I frequently have to explain to customers that a report is produced from dirty data and they should be aware of it, to which they would typically remark that a few errors will note make a huge difference, and they are right. But a lot of BI tools allow easy drill-down, and that is where they have to be careful.

Kaiser

Dimitri: Absolutely, drilling down often presents problems, usually because the original research design did not anticipate analysis at those levels. This post does not deal with drilling down, however.

J

"Another is that statistical techniques by definition generalize the data, and thus are not very sensitive to individual values."

This statement is a little over the top. The robustness of a statistical technique to outliers varies a good deal between methods and across sample sizes. Least squares regression like you're doing here is actually quite sensitive to anomalous data points in many circumstances.

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Working...
Your comment could not be posted. Error type:
Your comment has been posted. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.

Working...

Post a comment

Your Information

(Name is required. Email address will not be displayed with the comment.)

Marketing and advertising analytics expert. Author and Speaker. Currently at Vimeo and NYU. See my full bio.

Spring 2015 Courses (New York)

Jan 26: Business Analytics & Data Visualization (14 weeks) Info

Feb 23: Statistics for Management (10 weeks) Info

Mar 28: Careers in Business Analytics & Data Science (one-day seminar) Register

Apr 7: The Art of Data Visualization Workshop (6 weeks) Register

Next Events

Mar: 17 Finding and Telling Stories Using Data Visualization, Arlington, VA

Apr: 8 Princeton Association of New England, Boston, MA

Apr: 16-17 Data Visualization Workshop,, Digital Media Marketing Conference, St Louis, MO

Apr: 23 Marketing Modelers Group, NYC. Email for details.

May: 12 Finding and Telling Stories Using Data Visualization, Boston, MA

May: 29 Princeton Reunions Alumni-Faculty Forums, Princeton, NJ

Aug: 8-13 Joint Statistical Meetings, Seattle

Past Events

See here

Junk Charts Blog



Link to junkcharts

Graphics design by Amanda Lee

Search3

  • only in Big Data

Community