Does the “Table 1 fallacy” apply if it is Table S1 instead?
In a randomized experiment (i.e. RCT, A/B test, etc.) units are randomly assigned to treatments (i.e. conditions, variants, etc.). Let’s focus on Bernoulli randomized experiments for now, where each...
View ArticleHow different are causal estimation and decision-making?
Decision theory plays a prominent role in many texts and courses on theoretical statistics. However, the “decisions” being made are often as simple as using a particular estimator and then producing a...
View ArticleI had big plans for that four-fifths of a penny: False precision and fraud
Andrew likes to discourage false precision through reporting too many digits for estimates. I think this is good advice, especially for abstracts, summaries, and the primary outputs of much research. I...
View ArticleWhy not look at Y?
In some versions of a “design-based” perspective on causal inference, the idea is to focus on how units are assigned to different treatments (i.e. exposures, actions), rather than focusing on a model...
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Successful randomization and covariate “imbalance” in a survey experiment in...
Last year I wrote about the value of testing observable consequences of a randomized experiment having occurred as planned. For example, if the randomization was supposedly Bernoulli(1/2), you can...
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When plotting all the data can help avoid overinterpretation
Patrick Ruffini and David Weakliem both looked into this plot that’s been making the rounds, which seems to suggest a sudden drop in some traditional values: But the survey format changed between 2019...
View ArticlePostdoctoral position at MIT: privacy, synthetic data, fairness & causal...
I have appreciated Jessica’s recent coverage of differential privacy and related topics on this blog — especially as I’ve also started working in this general area. So I thought I’d share this new...
View ArticleEffect size expectations and common method bias
I think researchers in the social sciences often have unrealistic expectations about effect sizes. This has many causes, including publication bias (and selection bias more generally) and forking...
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New research on social media during the 2020 election, and my predictions
Back in 2020, leading academics and researchers at the company now known as Meta put together a large project to study social media and the 2020 US elections — particularly the roles of Instagram and...
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Mundane corrections to the dishonesty literature
There is a good deal of coverage of the more shocking reasons that papers on the psychology of dishonesty by Dan Ariely and Francesca Gino need to be corrected or retracted. I thought I’d share a more...
View ArticleFaculty position in computation & politics at MIT
We have this tenure-track Assistant Professor position open at MIT. It is an unusual opportunity in being a shared position between the Department of Political Science and the College of Computing. (I...
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Confusions about inference, prediction, and “probability of superiority”
People sometimes confuse certainty about summary statistics with certainty about draws from the distributions they summarize. Saying that we are quite confident that the average outcome for one group...
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thefacebook and mental health trends: Harvard and Suffolk County Community...
Multiple available measures indicate worsening mental health among US teenagers. Prominent researchers, commentators, and news sources have attributed this to effects of information and communication...
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Partisan assortativity in media consumption: Aggregation
How much do people consume news media that is mainly consumed by their co-partisans? And how do new media, including social media and their “dangerous algorithms“, contribute to this? One way of...
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Getting the first stage wrong
Sometimes when you conduct (or read) a study you learn you’re wrong in interesting ways. Other times, maybe you’re wrong for less interesting reasons. Being wrong about the “first stage” can be an...
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Relating t-statistics and the relative width of confidence intervals
How much does a statistically significant estimate tell us quantitatively? If you have an estimate that’s statistically distinguishable from zero with some t-statistic, what does that say about your...
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