Sunday, June 21, 2009

Excellence in reporting statistics

Sharon Begley of Newsweek received the Excellence in Statistical Reporting Award - Statistical Modeling, Causal Inference, and Social Science

I have to say, I didn't even know the ASA (a professional organization to which I belong and participate) gives out awards for reporting. With all the bad reporting involving statistics, it's refreshing to see a real effort for someone to try to help the public make sense of everything. Way to go Sharon!

Thursday, June 18, 2009

Test post from Inference for R

I am testing out the Inference for R blogging tool.

a <- 3



[1] 6

Pretty neat.

Sunday, June 14, 2009

Graphing the many dimensions of gay rights

Gay Rights are Popular in Many Dimensions - Statistical Modeling, Causal Inference, and Social Science

In addition to having an interesting message, the graph in the article is the most well done I've seen in a long time. The data to ink ratio is extremely high, and the amount of sheer data presented is astounding. Yet, the graph is clear and easy to read.

Friday, June 12, 2009

The IN VIVO Blog: Pfizer Deceives, While GSK Shines

The IN VIVO Blog: Pfizer Deceives, While GSK Shines

Dear Pfizer:

Cut it out. Really. You're making it harder for everybody, including those of us who are honestly trying to get drugs on the market for the patients. You're the 800 pound gorilla, and you need to take on your responsibility as role model, not the big spoiled kid who tricks the teacher.

Monday, June 1, 2009

The future of data visualization stupidity

What’s Wrong With this Graphic on the Future of Information?

O brave new world! That has such baloney in't! : Pharyngula


Ok, I don't know where to begin. X-axis, I guess. The really funky distortion on the x-axis distorts the areas of this graph so even if they meant something you couldn't make any inferences. The y-axis doesn't exist, making these areas meaningless even if you could rescale the x-axis. Even if you could find a common y-axis scale there is no differentiation between historical data and projection, but perhaps you could be astute to figure that out (no uncertainty intervals, though).

But there's an even more fundamental problem. The above graph is not about information at all, but rather media. So the graph is as much about information as its information content.

But you can take it even further. It looks like the poor "local marketplace" finally died out in 1998. But that ignores local marketplaces created by, say, social networks. Television stations have websites, blogs, and many even have social network sites. Newspapers and magazines have websites, blogs, and even social networks. To create such a sharp distinction in the graph above avoids a fundamental point about information media: that the lines are blurring, and will likely to stay blurred or even blur more in the future.

All in all, a useless graphic.