By Daniel J. Levitin
From The New York occasions bestselling writer of THE prepared brain and this can be YOUR mind ON track, a primer to the serious considering that's extra worthy now than ever.
We are bombarded with additional info every day than our brains can process—especially in election season. it is raining undesirable info, half-truths, or even outright lies. New York Times bestselling writer Daniel J. Levitin shows the way to realize deceptive bulletins, information, graphs, and written stories revealing the methods mendacity weasels can use them.
it really is turning into more durable to split the wheat from the electronic chaff. How will we distinguish incorrect information, pseudo-facts, distortions, and outright lies from trustworthy info? Levitin teams his box consultant into categories—statistical infomation and defective arguments—ultimately exhibiting how technology is the bedrock of severe pondering. Infoliteracy skill 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 seriously in regards to the phrases and numbers we stumble upon if we wish to succeed at paintings, at play, and in profiting from our lives. this suggests checking the plausibility and reasoning—not passively accepting info, repeating it, and making judgements in accordance with it. Readers discover ways to keep away from the extremes of passive gullibility and cynical rejection. Levitin's captivating, pleasing, available consultant can assist somebody get up to a complete lot of items that are not so. And capture a few mendacity weasels of their tracks!
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Additional resources for A Field Guide to Lies: Critical Thinking in the Information Age
The ecological fallacy is thinking that if you select someone at random from the group with the higher mean, that individual is likely to have a higher income. The neat thing is, in the examples above, that it’s not just the mean that is higher in Town B but also the median and the mode. ) As another example, it has been suggested that wealthy individuals are more likely to vote Republican, but evidence shows that the wealthier states tend to vote Democratic. The wealth of those wealthier states may be skewed by a small percentage of super-wealthy individuals.
But the room could have Mark Zuckerberg (net worth $35 billion) and ninety-nine people who are indigent. The average can smear across differences that are important. Another thing to watch out for in averages is the bimodal distribution. Remember, the mode is the value that occurs most often. In many biological, physical, and social datasets, the distribution has two or more peaks—that is, two or more values that appear more than the others. For example, a graph like this might show the amount of money spent on lunches in a week (x-axis) and how many people spent that amount (y-axis).
They are interpretations. And your interpretation may be just as good as, or better than, that of the person reporting them to you. 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).
A Field Guide to Lies: Critical Thinking in the Information Age by Daniel J. Levitin