Daniel J. Levitin's A Field Guide to Lies: Critical Thinking in the Information PDF

By Daniel J. Levitin

From The New York instances bestselling writer of THE prepared brain and this can be YOUR mind ON tune, a primer to the severe pondering that's extra beneficial now than ever.

We are bombarded with additional information every day than our brains can process—especially in election season. it really is raining undesirable info, half-truths, or even outright lies. New York Times bestselling writer Daniel J. Levitin shows how one can realize deceptive bulletins, facts, graphs, and written experiences revealing the methods mendacity weasels can use them.

it really is turning into tougher to split the wheat from the electronic chaff. How can we distinguish incorrect information, pseudo-facts, distortions, and outright lies from trustworthy details? Levitin teams his box advisor into categories—statistical infomation and defective arguments—ultimately exhibiting how technological know-how is the bedrock of severe considering. Infoliteracy capability figuring out that there are hierarchies of resource caliber and bias that variously distort our info feeds through each media channel, together with social media. We may well anticipate newspapers, bloggers, the govt., and Wikipedia to be factually and logically right, yet they so frequently will not be. we have to imagine significantly concerning the phrases and numbers we come across if we wish to prevail at paintings, at play, and in profiting from our lives. this suggests checking the plausibility and reasoning—not passively accepting details, repeating it, and making judgements in keeping with it. Readers discover ways to keep away from the extremes of passive gullibility and cynical rejection. Levitin's fascinating, pleasing, obtainable advisor will help somebody get up to an entire lot of items that are not so. And trap a few mendacity weasels of their tracks!

 

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Example text

Sometimes, the numbers are simply wrong, and it’s often easiest to start out by conducting some quick plausibility checks. After that, even if the numbers pass plausibility, three kinds of errors can lead you to believe things that aren’t so: how the numbers were collected, how they were interpreted, and how they were presented graphically. In your head or on the back of an envelope you can quickly determine whether a claim is plausible (most of the time). Don’t just accept a claim at face value; work through it a bit.

Here is an example from a poster presented at a conference by a student researcher, which looked like this (I’ve redrawn it here): What does all that mean? From the text on the poster itself (though not on this graph), we know that the researchers are studying brain activations in patients with schizophrenia (SZ). What are HCs?  .  . the units could be anything! What are we looking at? Scores on a test, levels of brain activations, number of brain regions activated? Number of Jell-O brand pudding cups they’ve eaten, or number of Johnny Depp movies they’ve seen in the last six weeks?

If the y-axis started at zero, the 13 percent would be apparent visually: Discontinuity in Vertical or Horizontal Axis Imagine a city where crime has been growing at a rate of 5 percent per year for the last ten years. You might graph it this way: Nothing wrong with that. But suppose that you’re selling home security systems and so you want to scare people into buying your product. Using all the same data, just create a discontinuity in your x-axis. This will distort the truth and deceive the eye marvelously: Here, the visual gives the impression that crime has increased dramatically.

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