« The New Zealand sun alibi | Main | Lance Armstrong beat statistical odds, but it's not enough »

Comments

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

Jordan G

That's kind of creepy. I'm not (entirely) doubting the ability for a survey to accurately predict which candidates are less likely to file for disability claims, I'm just wondering on what basis do the survey developers and business owner suggest that such claims are illegitimate. Some workers may have higher feelings of loyalty to their companies such that they are less likely to file disability claims, but that a company would rather higher "yes men" than payout potentially legitimate claims caused (on the job) to their workers should be an ethical concern to the survey developers, the article’s author, and WSJ's readers.

I question the degree to which we, as a society, privilege quantitative data and prediction models. We seem to accept all such products as if they were providing given, a priori observations requiring no further dissection, investigation, or rigor. We're going to get ourselves into a lot of trouble soon, I'm afraid.

Kaiser

Jordan: I haven't read Nathan Silver's book but it seems like he's bucking the trend to tell us why most predictions fail.

Phil H

Surely it's self-defeating to create a model of the ideal candidate? Sure, you can create a list of ideals and compare applicants against it. If you ignore experience there, you'll get no experienced people because they're more expensive.

Presumably they have measured existing employees and done the statistical analysis on what were the strongest predictors of quality. That then hinges on what factors were measured -- was race one of them? It also depends on the appropriateness of the quality measure.

If you measure the number of calls handled per hour I can show you a room full of monkeys that will make your model ecstatic.

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

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

Next Events

Mar: 26 Agilone Webinar "How to Build Data Driven Marketing Teams"

Apr: 4 Analytically Speaking Webcast, by JMP, with Alberto Cairo

May: 19-21 Midwest Biopharmaceutical Statistics Workshop, Muncie, IN

May: 25-28 Statistical Society of Canada Conference, Toronto

June: 16-19 Predictive Analytics World (Keynote), Chicago



Past Events

Feb: 27 Data-Driven Marketing Summit by Agilone, San Francisco

Dec: 12 Brand Innovators Big Data Event

Nov: 20 NC State Invited Big Data Seminar

Nov 5: Social Media Today Webinar

Nov: 1 LISA Conference

Oct: 29 NYU Coles Science Center

Oct: 9 Princeton Tech Meetup

Oct: 8 NYU Bookstore, NYC

Sep: 18 INFORMS NYC

Jul: 30 BIG Frontier, Chicago

May: 30 Book Expo, NYC

Apr: 4 New York Public Library Labs and Leaders in Software and Art Data Viz Panel, NYC

Mar: 22 INFORMS NY Student-Practitioner Forum on Analytics, NYC

Oct: 19 Predictive Analytics World, NYC

Jul: 30 JSM, Miami

Junk Charts Blog



Link to junkcharts

Graphics design by Amanda Lee

Search3

  • only in Big Data

Community