|Scooped by Sharrock|
Data, in the wrong hands, whether malicious, manipulative or naïve can be downright dangerous. Indeed, when big data goes bad it can be lethal. Unfortunately the learning game is no stranger to both the abuse of data. Here’s six examples showing seven species of ‘bad data’.
This excerpt kills me:1. Data subtraction: Ken RobinsonDon’t let the selective graphical representation of data, destroy the integrity of the data. A good example of blatant data editing is the memorable ‘ritalin’ image used by Sir Ken Robinson in his TED talk at 3.47. This image is taken from its RSA animation.Compare Robinson’s graph with the true source.His has no legend and he’s recalibrated states to look as if there’s zero prescriptions. To understand this data you have to look at its source to understand that the white areas represent states that did NOT participate in the study or did not have reported prescription data. It’s a distortion, an exaggeration to help make a point that the data doesn’t really supportIn fact, much of what passes for fact in Sir Ken Robinson’s TED talks are not supported by any research or data whatsoever.