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

By Ben Goldacre

Have you questioned how at some point the media can assert that alcohol is undesirable for us and the subsequent unashamedly run a narrative touting the advantages of day-by-day alcohol intake? Or how a drug that's pulled off the marketplace for inflicting center assaults ever acquired licensed within the first position? How can ordinary readers, who aren’t docs or Ph.D.s in biochemistry, inform what they need to be being attentive to 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 stories. He has additionally taken the media to activity for its willingness to throw proof and evidence out the window. yet he’s no longer right here simply to let you know what’s unsuitable. Goldacre is the following to educate you ways to judge placebo results, double-blind experiences, and pattern sizes, so you might realize undesirable technology should you see it. You’re approximately to believe plenty higher.

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Extra resources for Bad Science: Quacks, Hacks, and Big Pharma Flacks

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1) cannot be connected by an arc, while this is not necessarily the case in a convergent connection. 1 Inductive Causation Algorithm 1. For each pair of variables A and B in V search for set SAB ⊂ V (including S = ∅) such that A and B are independent given SAB and A, B ∈ / SAB . If there is no such a set, place an undirected arc between A and B. 2. For each pair of non-adjacent variables A and B with a common neighbor C, check whether C ∈ SAB . If this is not true, set the direction of the arcs A − C and C − B to A → C and C ← B.

Such arcs are called compelled, since their direction is determined by the equivalence class even though they are not part of any v-structure. Changing the direction of any other, non-compelled arc results in another network in the same equivalence class as long as it does not introduce any new v-structure or in any cycle. 2 is the Markov blanket (Pearl, 1988). It essentially represents the set of nodes that completely d-separates a given node from the rest of the graph. 3 (Markov blanket). The Markov blanket of a node A ∈ V is the minimal subset S of V such that A⊥ ⊥P V − S − A |S.

R, j = 1, . . ,C, k = 1, . . , L} for the random variables X and Y and all the configurations of the conditioning variables Z. Two common conditional independence tests are the following: • Mutual information (Cover and Thomas, 2006), an information-theoretic distance measure defined as R C L ni jk n++k ni jk log . 10) It is proportional to the log-likelihood ratio test G2 (they differ by a 2n factor, where n is the sample size), and it is related to the deviance of the tested models. • The classic Pearson’s X 2 test for contingency tables, R X2 (X,Y | Z) = ∑ C L ∑∑ i=1 j=1 k=1 ni jk − mi jk mi jk 2 , where mi jk = ni+k n+ jk .

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