Teacher Tools and Tips
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# Teacher Tools and Tips

Tools, tips and practices to share with teachers
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## Donald Clark Plan B: When Big Data goes bad: 6 epic fails

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’.
Sharrock's insight:

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.
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## “Statisticians are the modern explorers.” An interview with Professor David J. Hand - Statistics Views

You can’t go wrong with a career in statistics. Most statisticians start out with a degree in maths but some of the best I’ve known started off as biochemists or economists or in some other area, and then have retrained as statisticians. Since they have an understanding of a particular problem domain they can have an advantage in analysing data in that area.

But it is important to remember that statistics is not simply a branch of mathematics. You get 13 year old prodigies in mathematics but not in statistics because to be a good statistician you need to understand more than the mathematics. I know many first class mathematicians who simply cannot do statistics.

Sharrock's insight:

from the article: "We need statisticians to cope with the data for medical research, government policies, engineering and there have not been enough. Society would benefit tremendously from statisticians and there is a danger that there aren’t going to be enough."

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