Category: Sociology

The bullshit asymmetry principle

Jordan Anaya writes, “We talk about this concept a lot, I didn’t realize there was a name for it.” From the wikipedia entry: Publicly formulated the first time in January 2013 by Alberto Brandolini, an Italian programmer, the bullshit asymmetry principle (also known as Brandolini’s law) states that: The amount of energy needed to refute […]

Storytelling: What’s it good for?

A story can be an effective way to send a message. Anna Clemens explains: Why are stories so powerful? To answer this, we have to go back at least 100,000 years. This is when humans started to speak. For the following roughly 94,000 years, we could only use spoken words to communicate. Stories helped us […]

Did she really live 122 years?

Even more famous than “the Japanese dude who won the hot dog eating contest” is “the French lady who lived to be 122 years old.” But did she really? Paul Campos writes: Here’s a statistical series, laying out various points along the 100 longest known durations of a particular event, of which there are billions […]

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What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

The post What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis? appeared first on Statistical Modeling, Causal Inference, and Social Science.

What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis?

Someone writes: I would like to ask you for an advice regarding obtaining data for reanalysis purposes from an author who has multiple papers with statistical errors and doesn’t want to share the data. Recently, I reviewed a paper that included numbers that had some of the reported statistics that were mathematically impossible. As the […]

The post What to do when you read a paper and it’s full of errors and the author won’t share the data or be open about the analysis? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

The post Combining apparently contradictory evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

The post Combining apparently contradictory evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

Combining apparently contradictory evidence

I want to write a more formal article about this, but in the meantime here’s a placeholder. The topic is the combination of apparently contradictory evidence. Let’s start with a simple example: you have some ratings on a 1-10 scale. These could be, for example, research proposals being rated by a funding committee, or, umm, […]

The post Combining apparently contradictory evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.”

What better day than Christmas, that day of gift-giving, to discuss “loss aversion,” the purported asymmetry in utility, whereby losses are systematically more painful than gains are pleasant? Loss aversion is a core principle of the heuristics and biases paradigm of psychology and behavioral economics. But it’s been controversial for a long time. For example, […]

The post “Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.”

What better day than Christmas, that day of gift-giving, to discuss “loss aversion,” the purported asymmetry in utility, whereby losses are systematically more painful than gains are pleasant? Loss aversion is a core principle of the heuristics and biases paradigm of psychology and behavioral economics. But it’s been controversial for a long time. For example, […]

The post “Thus, a loss aversion principle is rendered superfluous to an account of the phenomena it was introduced to explain.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Carol Nickerson explains what those mysterious diagrams were saying

A few years ago, James Coyne asked, “Can you make sense of this diagram?” and I responded, No, I can’t. At the time, Carol Nickerson wrote up explanations for two of the figures in the article in question. So if anyone’s interested, here they are: Carol Nickerson’s explanation of Figure 2 in Kok et al. […]

The post Carol Nickerson explains what those mysterious diagrams were saying appeared first on Statistical Modeling, Causal Inference, and Social Science.

Carol Nickerson explains what those mysterious diagrams were saying

A few years ago, James Coyne asked, “Can you make sense of this diagram?” and I responded, No, I can’t. At the time, Carol Nickerson wrote up explanations for two of the figures in the article in question. So if anyone’s interested, here they are: Carol Nickerson’s explanation of Figure 2 in Kok et al. […]

The post Carol Nickerson explains what those mysterious diagrams were saying appeared first on Statistical Modeling, Causal Inference, and Social Science.