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Adults struggle to solve puzzle kids solve with ease

February 5, 2016 By editor

A National Geographic puzzle (described below) that 80% of children can solve flummoxes most adults.

As we get older we develop a whole range of skills that allow us to operate more efficiently. The problem is that these optimizations result in blind spots and lowering of creativity.

Now, don’t get me wrong—the tradeoff is worthwhile. You really don’t want a six-year-old in charge of your data analysis strategy. Creativity is over emphasised in modern business writing. However, clearly we should try to eliminate some our harmless biases.

This is where decision and data science help. Mathematical models, formal decision making processes and real-world data can challenge our preconceptions. When confronted with a gut-feeling square peg that won’t go into the round hole of a formal model we are forced to confront our biases and re-frame our understanding of our environment.

Alternatively, we can angrily toss out the model and data for disagreeing with our cherished theories—but that’s politics for you.

The puzzle

The puzzle is to work out which way the bus shown at the top of the post is traveling.

Filed Under: Decision science Tagged With: bias, creativity

Election polling errors blamed on bias

January 19, 2016 By editor

UK polling station sign

A report has concluded that the spectacular failure of pollsters to predict the result of the 2015 UK Parliamentary elections was largely due to

systematic over-representation of Labour voters and under-representation of Conservative voters

The report, compiled by a panel of academics and statisticians, was commissioned by the polling industry to determine why they had predicted a “photo-finish” in an election where Conservatives outpolled Labour by 36.9% to 30.4%—a crushing defeat for Labour that lead to the resignation of their leader.

Pollsters apparently used collection methods that were more likely to be used by young (Labour-leaning) voters than older (Conservative-leaning) voters. Frankly, not realising that online surveys are going to under-represent the over 70s is a shocking oversight.

While betting markets also under-estimated the extent of the win, they did better than the polling industry—without the expense.

Filed Under: Data analysis, Data science Tagged With: betting, bias, polling, prediction market

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