andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2284 knowledge-graph by maker-knowledge-mining
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Introduction: In “Story: A Definition,” visual analysis researcher Robert Kosara writes : A story ties facts together. There is a reason why this particular collection of facts is in this story, and the story gives you that reason. provides a narrative path through those facts. In other words, it guides the viewer/reader through the world, rather than just throwing them in there. presents a particular interpretation of those facts. A story is always a particular path through a world, so it favors one way of seeing things over all others. The relevance of these ideas to statistical graphics is apparent. From a completely different direction, in “When do stories work? Evidence and illustration in the social sciences,” Thomas Basbøll and I write : Storytelling has long been recognized as central to human cognition and communication. Here we explore a more active role of stories in social science research, not merely to illustrate concepts but also to develop new ideas and evalu
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1 In “Story: A Definition,” visual analysis researcher Robert Kosara writes : A story ties facts together. [sent-1, score-0.578]
2 There is a reason why this particular collection of facts is in this story, and the story gives you that reason. [sent-2, score-0.58]
3 In other words, it guides the viewer/reader through the world, rather than just throwing them in there. [sent-4, score-0.198]
4 presents a particular interpretation of those facts. [sent-5, score-0.194]
5 A story is always a particular path through a world, so it favors one way of seeing things over all others. [sent-6, score-0.618]
6 The relevance of these ideas to statistical graphics is apparent. [sent-7, score-0.496]
7 From a completely different direction, in “When do stories work? [sent-8, score-0.314]
8 Evidence and illustration in the social sciences,” Thomas Basbøll and I write : Storytelling has long been recognized as central to human cognition and communication. [sent-9, score-0.5]
9 Here we explore a more active role of stories in social science research, not merely to illustrate concepts but also to develop new ideas and evaluate hypotheses, for example in deciding that a research method is effective. [sent-10, score-1.035]
10 We draw a connection to posterior predictive checking, which I earlier had argued is fundamentally connected with statistical graphics and exploratory data analysis (see this paper from 2003 and this one 2004). [sent-12, score-0.612]
11 I just wanted to juxtapose these two perspectives, each of which connect statistical graphics to literature, but in a different way. [sent-14, score-0.505]
12 Kosara focuses on the idea that stories have narrative and viewpoint, and Basbøll and I focus on the idea that effective stories are anomalous and immutable. [sent-15, score-1.187]
13 All these ideas seem important to me, and it would be interesting to think about how they fit together. [sent-16, score-0.139]
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Introduction: In “Story: A Definition,” visual analysis researcher Robert Kosara writes : A story ties facts together. There is a reason why this particular collection of facts is in this story, and the story gives you that reason. provides a narrative path through those facts. In other words, it guides the viewer/reader through the world, rather than just throwing them in there. presents a particular interpretation of those facts. A story is always a particular path through a world, so it favors one way of seeing things over all others. The relevance of these ideas to statistical graphics is apparent. From a completely different direction, in “When do stories work? Evidence and illustration in the social sciences,” Thomas Basbøll and I write : Storytelling has long been recognized as central to human cognition and communication. Here we explore a more active role of stories in social science research, not merely to illustrate concepts but also to develop new ideas and evalu
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Introduction: See here for the full story.
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Introduction: In “Story: A Definition,” visual analysis researcher Robert Kosara writes : A story ties facts together. There is a reason why this particular collection of facts is in this story, and the story gives you that reason. provides a narrative path through those facts. In other words, it guides the viewer/reader through the world, rather than just throwing them in there. presents a particular interpretation of those facts. A story is always a particular path through a world, so it favors one way of seeing things over all others. The relevance of these ideas to statistical graphics is apparent. From a completely different direction, in “When do stories work? Evidence and illustration in the social sciences,” Thomas Basbøll and I write : Storytelling has long been recognized as central to human cognition and communication. Here we explore a more active role of stories in social science research, not merely to illustrate concepts but also to develop new ideas and evalu
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Introduction: Nick Brown is bothered by this article , “An unscented Kalman filter approach to the estimation of nonlinear dynamical systems models,” by Sy-Miin Chow, Emilio Ferrer, and John Nesselroade. The introduction of the article cites a bunch of articles in serious psych/statistics journals. The question is, are such advanced statistical techniques really needed, or even legitimate, with the kind of very rough data that is usually available in psych applications? Or is it just fishing in the hope of discovering patterns that are not really there? I wrote: It seems like a pretty innocuous literature review. I agree that many of the applications are silly (for example, they cite the work of the notorious John Gottman in fitting a predator-prey model to spousal relations (!)), but overall they just seem to be presenting very standard ideas for the mathematical-psychology audience. It’s not clear whether advanced techniques are always appropriate here, but they come in through a natura
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Introduction: Dan Silitonga writes: I was wondering whether you would have any advice on building a regression model on a very small datasets. I’m in the midst of revamping the model to predict tax collections from unincorporated businesses. But I only have 27 data points, 27 years of annual data. Any advice would be much appreciated. My reply: This sounds tough, especially given that 27 years of annual data isn’t even 27 independent data points. I have various essentially orthogonal suggestions: 1 [added after seeing John Cook's comment below]. Do your best, making as many assumptions as you need. In a Bayesian context, this means that you’d use a strong and informative prior and let the data update it as appropriate. In a less formal setting, you’d start with a guess of a model and then alter it to the extent that your data contradict your original guess. 2. Get more data. Not by getting information on more years (I assume you can’t do that) but by breaking up the data you do
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