Data Visualization is not a ‘magic bullet’ solution to the need for more evidence informed decision-making. So what are the instances in which data visualization is an appropriate form of research communication? What are the ways to enhance the effectiveness of data visualization? Imogen Robinson from SciDev.Net looks for answers to these questions.
Can Wikipedia help us understand statistical concepts such as Central Limit Theorem? Sure, if you would like to know the mathematical definition and the theory. But if you want to explain central limit theorem to a 10 year-old, a much better resource is an interactive visualization by Michael Freeman. Watch how large number of sample means when plotted on a chart form a perfectly shaped bell curve!
How do we read pie charts? Do they differ from the even more reviled donut charts? In two papers presented at EuroVis, Drew Skau and Robert Kosara show that the common wisdom about how we read these charts (by angle) is almost certainly wrong, and that things are much more complicated than we thought.
If you make charts on the internet, angry email about those charts is inevitable. Especially if your charts sometimes use a y-axis that starts at a number other than zero. You see, an old book called How to Lie With Statistics has convinced people that truncated axes are a devilish tool of deception.