The importance of simulating the extremes Simulation is commonly used by statisticians/data analysts to: (1) estimate variability/improve predictors , (2) to evaluate the space of potential outcomes , and (3) to evaluate the properties of new algorithms or procedures. Over the last couple of days, discussions of simulation have popped up in a couple of different places. First, the reviewers of a paper that my student is working on had asked a question about the behavior of the method in different conditions. I mentioned in passing, that I thought it was a good idea to simulate some cases where our method will definitely break down.
37 Data-ish Blogs You might not know it, but there are actually a ton of data and visualization blogs out there. I'm a bit of a feed addict subscribing to just about anything with a chart or a mention of statistics on it (and naturally have to do some feed-cleaning every now and then). In a follow up to my short list last year, here are the data-ish blogs, some old and some new, that continue to post interesting stuff. Data and Statistics By the Numbers - Column from The New York Times visual Op-ed columnist, Charles Blow, who also used to be NYT's graphics director.Data Mining - Matthew Hurst, scientist at Microsoft's MSN, also the co-creator of BlogPulse.Statistical Modeling - We might disagree on certain things, but Andrew's blog is one of the few active pure statistics blogs.The Numbers Guy - Data-minded reporting from Carl Bialik of the Wall Street Journal.Basketball Geek - Like statistical analysis and basketball?
Mike Kruzeniski – How Print Design is the Future of Interaction This post describes “How Print Design is the Future of Interaction,” a talk I gave at SXSW Interactive on March 12, 2011. The slides from the talk are available to view on Slideshare, and you can see some of the discussion that followed on Twitter here. Introduction There are three areas that I covered in the talk. First, how the visual language of UI has evolved and been shaped in to what we find in the interfaces we are familiar with today. Technical Methods Report: Guidelines for Multiple Testing in Impact Evaluations - Appendix B: Introduction to Multiple Testing This appendix introduces the hypothesis testing framework for this report, the multiple testing problem, statistical methods to adjust for multiplicity, and some concerns that have been raised about these solutions. The goal is to provide an intuitive, nontechnical discussion of key issues related to this complex topic to help education researchers apply the guidelines presented in the report. A comprehensive review of the extensive literature in this area is beyond the scope of this introductory discussion. The focus is on continuous outcomes, but appropriate procedures are highlighted for other types of outcomes (such as binary outcomes). The appendix concludes with recommended methods.1
Four Easy Visualization Mistakes to Avoid Creating a great visualization is not as hard as it seems. Provided you have some interesting data and an effective tool with which to visualize it, a little bit of thoughtful design will lead to a decent result. That said, there are some mistakes that are very easy to make, but can ruin even a thoughtfully-made piece. Here are four data visualization mistakes you should avoid. 1.
Statistical significance for genomewide studies Author Affiliations Edited by Philip P. Green, University of Washington School of Medicine, Seattle, WA, and approved May 30, 2003 (received for review January 28, 2003) Abstract The top 20 data visualisation tools One of the most common questions I get asked is how to get started with data visualisations. Beyond following blogs, you need to practise – and to practise, you need to understand the tools available. In this article, I want to introduce you to 20 different tools for creating visualisations: from simple charts to complex graphs, maps and infographics. Almost everything here is available for free, and some you have probably installed already. Advertisement Entry-level tools
20+ Tools to Create Your Own Infographics A picture is worth a thousand words – based on this, infographics would carry hundreds of thousands of words, yet if you let a reader choose between a full-length 1000-word article and an infographic that needs a few scroll-downs, they’d probably prefer absorbing information straight from the infographic. What’s not to like? Colored charts and illustrations deliver connections better than tables and figures and as users spend time looking back and forth the full infographic, they stay on the site longer.