Download Bad Science: Quacks, Hacks, and Big Pharma Flacks by Ben Goldacre PDF

By Ben Goldacre

Have you puzzled how sooner or later the media can assert that alcohol is undesirable for us and the subsequent unashamedly run a narrative touting some great benefits of day-by-day alcohol intake? Or how a drug that's pulled off the marketplace for inflicting middle assaults ever bought licensed within the first position? How can typical readers, who aren’t doctors or Ph.D.s in biochemistry, inform what they need to be taking note of and what’s, good, simply extra bullshit?

Ben Goldacre has made some extent of revealing quack medical professionals and nutritionists, bogus credentialing courses, and biased medical reports. He has additionally taken the media to job for its willingness to throw proof and facts out the window. yet he’s no longer right here simply to let you know what’s fallacious. Goldacre is the following to coach you the way to judge placebo results, double-blind reports, and pattern sizes, so you might realize undesirable technological know-how in case you see it. You’re approximately to suppose an awful lot greater.

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Additional info for Bad Science: Quacks, Hacks, and Big Pharma Flacks

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Wilson, Judith. html, accessed September 18, 2012. CHAPTER 3 Asteroid Belts and Spandex Cars: Using Descriptive Statistics to Answer Your Most Weighty Questions Dear Mom, I hope you’re doing well. I haven’t heard from you in over two weeks, and I worry you’re still mad at me about the incident. I’d prefer to talk to you in person, but you don’t seem to be getting my phone calls and you didn’t answer the door when I visited. So, I must resort to explaining myself in an email. First, when I dropped by your house with my new book project, I had no idea it would cause so much trouble.

Your mama’s so heavy, she’s been zoned for commercial development. 1 These three descriptions paint very different pictures, and if you were to use these descriptions to estimate the woman’s size, you’d most likely come up with three very different answers. In other words, you’d have variation in your data.  . by Hugh Payne © 2007 used with the permission of Black Dog & Leventhal Publishers. They Mama 25 variation, or differences between obser­vations. So how do you describe a dataset in the presence of variation?

For example, suppose you’re analyzing the hair color of a group of ladies. You might take each lady and categorize her into one of a few groups: blonde, brown, red, black, and gray. The color brown isn’t larger or smaller than red. It’s just different. And without a mathematical relationship between observations, we’re somewhat limited in our ability to mathematically summarize qualitative data. Quantitative observations, on the other hand, are meaningful Qualitative versus Quantitative Data 27 numerical values and so they can be sorted.

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