Jeff Lax writes: I’m probably not the only one telling you about this Science story, but just in case. The link points to a new research article reporting a failed replication of a study from 2008. The journal that published that now-questionable result refuses to consider publishing the replication attempt. My reply: I agree it’s […]

# Author: Andrew

## Causal inference: I recommend the classical approach in which an observational study is understood in reference to a hypothetical controlled experiment

Amy Cohen asked me what I thought of this article, “Control of Confounding and Reporting of Results in Causal Inference Studies: Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals,” by David Lederer et al. I replied that I liked some of their recommendations (downplaying p-values, graphing raw data, presenting results clearly) […]

## The publication asymmetry: What happens if the New England Journal of Medicine publishes something that you think is wrong?

After reading my news article on the replication crisis, retired cardiac surgeon Gerald Weinstein wrote: I have long been disappointed by the quality of research articles written by people and published by editors who should know better. Previously, I had published two articles on experimental design written with your colleague Bruce Levin [of the Columbia […]

## We’re done with our Applied Regression final exam (and solution to question 15)

We’re done with our exam. And the solution to question 15: 15. Consider the following procedure. • Set n = 100 and draw n continuous values x_i uniformly distributed between 0 and 10. Then simulate data from the model y_i = a + bx_i + error_i, for i = 1,…,n, with a = 2, b […]

## Algorithmic bias and social bias

The “algorithmic bias” that concerns me is not so much a bias in an algorithm, so much as a social bias resulting from the demand for, and expectation of, certainty.

## Pharmacometrics meeting in Paris on the afternoon of 11 July 2019

Julie Bertrand writes: The pharmacometrics group led by France Mentre (IAME, INSERM, Univ Paris) is very pleased to host a free ISoP Statistics and Pharmacometrics (SxP) SIG local event at Faculté Bichat, 16 rue Henri Huchard, 75018 Paris, on Thursday afternoon the 11th of July 2019. It will features talks from Professor Andrew Gelman, Univ […]

## Question 15 of our Applied Regression final exam (and solution to question 14)

Here’s question 15 of our exam: 15. Consider the following procedure. • Set n = 100 and draw n continuous values x_i uniformly distributed between 0 and 10. Then simulate data from the model y_i = a + bx_i + error_i, for i = 1,…,n, with a = 2, b = 3, and independent errors […]

## Question 14 of our Applied Regression final exam (and solution to question 13)

Here’s question 14 of our exam: 14. You are predicting whether a student passes a class given pre-test score. The fitted model is, Pr(Pass) = logit^−1(a_j + 0.1x), for a student in classroom j whose pre-test score is x. The pre-test scores range from 0 to 50. The a_j’s are estimated to have a normal […]

## Naomi Wolf and David Brooks

Palko makes a good point: Parul Sehgal has a devastating review of the latest from Naomi Wolf, but while Sehgal is being justly praised for her sharp and relentless treatment of her subject, she stops short before she gets to the most disturbing and important implication of the story. There’s an excellent case made here […]

## Question 13 of our Applied Regression final exam (and solution to question 12)

Here’s question 13 of our exam: 13. You fit a model of the form: y ∼ x + u full + (1 | group). The estimated coefficients are 2.5, 0.7, and 0.5 respectively for the intercept, x, and u full, with group and individual residual standard deviations estimated as 2.0 and 3.0 respectively. Write the […]

## Question 12 of our Applied Regression final exam (and solution to question 11)

Here’s question 12 of our exam: 12. In the regression above, suppose you replaced height in inches by height in centimeters. What would then be the intercept and slope of the regression? (One inch is 2.54 centimeters.) And the solution to question 11: 11. We defined a new variable based on weight (in pounds): heavy […]

## How statistics is used to crush (scientific) dissent.

Lakeland writes: When we interpret powerful as political power, I think it’s clear that Classical Statistics has the most political power, that is, the power to get people to believe things and change policy or alter funding decisions etc… Today Bayes is questioned at every turn, and ridiculed for being “subjective” with a focus on […]

## Question 11 of our Applied Regression final exam (and solution to question 10)

Here’s question 11 of our exam: 11. We defined a new variable based on weight (in pounds): heavy 200 and then ran a logistic regression, predicting “heavy” from height (in inches): glm(formula = heavy ~ height, family = binomial(link = “logit”)) coef.est coef.se (Intercept) -21.51 1.60 height 0.28 0.02 — n = 1984, k = […]

## Question 10 of our Applied Regression final exam (and solution to question 9)

Here’s question 10 of our exam: 10. For the above example, we then created indicator variables, age18_29, age30_44, age45_64, and age65up, for four age categories. We then fit a new regression: lm(formula = weight ~ age30_44 + age45_64 + age65up) coef.est coef.se (Intercept) 157.2 5.4 age30_44TRUE 19.1 7.0 age45_64TRUE 27.2 7.6 age65upTRUE 8.5 8.7 n […]

## Question 9 of our Applied Regression final exam (and solution to question 8)

Here’s question 9 of our exam: 9. We downloaded data with weight (in pounds) and age (in years) from a random sample of American adults. We created a new variables, age10 = age/10. We then fit a regression: lm(formula = weight ~ age10) coef.est coef.se (Intercept) 161.0 7.3 age10 2.6 1.6 n = 2009, k […]

## Question 8 of our Applied Regression final exam (and solution to question 7)

Here’s question 8 of our exam: 8. Out of a random sample of 50 Americans, zero report having ever held political office. From this information, give a 95% confidence interval for the proportion of Americans who have ever held political office. And the solution to question 7: 7. You conduct an experiment in which some […]

## Question 7 of our Applied Regression final exam (and solution to question 6)

Here’s question 7 of our exam: 7. You conduct an experiment in which some people get a special get-out-the-vote message and others do not. Then you follow up with a sample, after the election, to see if they voted. If you follow up with 500 people, how large an effect would you be able to […]

## Question 6 of our Applied Regression final exam (and solution to question 5)

Here’s question 6 of our exam: 6. You are applying hierarchical logistic regression on a survey of 1500 people to estimate support for a federal jobs program. The model is fit using, as a state-level predictor, the Republican presidential vote in the state. Which of the following two statements is basically true? (a) Adding a […]

## Question 5 of our Applied Regression final exam (and solution to question 4)

Here’s question 5 of our exam: 5. You have just graded an exam with 28 questions and 15 students. You fit a logistic item-response model estimating ability, difficulty, and discrimination parameters. Which of the following statements are basically true? (a) If a question is answered correctly by students with low ability, but is missed by […]

## Tony nominations mean nothing

Someone writes: I searched up *Tony nominations mean nothing* and I found nothing. So I had to write this. There are currently 41 theaters that the Tony awards accept when nominating their choices. If we are being as generous as possible, we could say that every one of those theaters will be hosting a performance […]