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2155 andrew gelman stats-2013-12-31-No on Yes-No decisions


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Introduction: Just to elaborate on our post from last month (“I’m negative on the expression ‘false positives’”), here’s a recent exchange exchange we had regarding the relevance of yes/no decisions in summarizing statistical inferences about scientific questions. Shravan wrote : Isn’t it true that I am already done if P(theta>0) is much larger than P(theta<0)? I don't need to compute any loss function if the former is 0.99 and the latter 0.01. In most studies of the type that people like me do [Shravan is a linguist], we set up experiments where we have a decisive test like this for theory A and against theory B. To which I replied : In some way the problem is with the focus on “theta.” Effects (and, more generally, comparisons) vary, they can be positive for some people in some settings and negative for other people in other settings. If you’re talking about a single “theta,” you have to define what population and what scenario you are thinking about. And it’s probably not the popul


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 Just to elaborate on our post from last month (“I’m negative on the expression ‘false positives’”), here’s a recent exchange exchange we had regarding the relevance of yes/no decisions in summarizing statistical inferences about scientific questions. [sent-1, score-1.387]

2 Shravan wrote : Isn’t it true that I am already done if P(theta>0) is much larger than P(theta<0)? [sent-2, score-0.123]

3 I don't need to compute any loss function if the former is 0. [sent-3, score-0.365]

4 In most studies of the type that people like me do [Shravan is a linguist], we set up experiments where we have a decisive test like this for theory A and against theory B. [sent-6, score-0.737]

5 To which I replied : In some way the problem is with the focus on “theta. [sent-7, score-0.152]

6 ” Effects (and, more generally, comparisons) vary, they can be positive for some people in some settings and negative for other people in other settings. [sent-8, score-0.593]

7 If you’re talking about a single “theta,” you have to define what population and what scenario you are thinking about. [sent-9, score-0.934]

8 And it’s probably not the population of Mechanical Turk participants and the scenario of an online survey. [sent-10, score-0.934]

9 If an effect is very small and positive in one population in one scenario, there’s no real reason to be confident that it will be positive in a different population in a different scenario. [sent-11, score-1.207]


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Introduction: Just to elaborate on our post from last month (“I’m negative on the expression ‘false positives’”), here’s a recent exchange exchange we had regarding the relevance of yes/no decisions in summarizing statistical inferences about scientific questions. Shravan wrote : Isn’t it true that I am already done if P(theta>0) is much larger than P(theta<0)? I don't need to compute any loss function if the former is 0.99 and the latter 0.01. In most studies of the type that people like me do [Shravan is a linguist], we set up experiments where we have a decisive test like this for theory A and against theory B. To which I replied : In some way the problem is with the focus on “theta.” Effects (and, more generally, comparisons) vary, they can be positive for some people in some settings and negative for other people in other settings. If you’re talking about a single “theta,” you have to define what population and what scenario you are thinking about. And it’s probably not the popul

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Introduction: The title of this post is silly but I have an important point to make, regarding an implicit model which I think many people assume even though it does not really make sense. Following a link from Sanjay Srivastava, I came across a post from David Funder saying that it’s useful to talk about the sizes of effects (I actually prefer the term “comparisons” so as to avoid the causal baggage) rather than just their signs. I agree , and I wanted to elaborate a bit on a point that comes up in Funder’s discussion. He quotes an (unnamed) prominent social psychologist as writing: The key to our research . . . [is not] to accurately estimate effect size. If I were testing an advertisement for a marketing research firm and wanted to be sure that the cost of the ad would produce enough sales to make it worthwhile, effect size would be crucial. But when I am testing a theory about whether, say, positive mood reduces information processing in comparison with negative mood, I am worried abou

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