# Posts Tagged ‘ Miscellaneous Statistics ’

## “We are moving from an era of private data and public analyses to one of public data and private analyses. Just as we have learned to be cautious about data that are missing, we may have to be cautious about missing analyses also.”

March 1, 2014
By

Stephen Senn writes: For many years now I [Senn] have been making the point that obtaining a license to market a drug should carry with it the obligation to share the results with interested parties. . . . Amongst those misunderstanding the issues, are many who work in the pharmaceutical industry. A common assumption is […]The post “We are moving from an era of private data and public analyses to…

## God/leaf/tree

February 28, 2014
By

Govind Manian writes: I wanted to pass along a fragment from Lichtenberg’s Waste Books — which I am finding to be great stone soup — that reminded me of God is in Every Leaf: To the wise man nothing is great and nothing small…I believe he could write treatises on keyholes that sounded as weighty […]The post God/leaf/tree appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Econometrics, political science, epidemiology, etc.: Don’t model the probability of a discrete outcome, model the underlying continuous variable

February 26, 2014
By

This is an echo of yesterday’s post, Basketball Stats: Don’t model the probability of win, model the expected score differential. As with basketball, so with baseball: as the great Bill James wrote, if you want to predict a pitcher’s win-loss record, it’s better to use last year’s ERA than last year’s W-L. As with basketball […]The post Econometrics, political science, epidemiology, etc.: Don’t model the probability of a discrete outcome,…

## “Edlin’s rule” for routinely scaling down published estimates

February 24, 2014
By

A few months ago I reacted (see further discussion in comments here) to a recent study on early childhood intervention, in which researchers Paul Gertler, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeerch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor estimated that a particular intervention on young children had raised incomes on young adults […]The post “Edlin’s rule” for routinely scaling down published estimates appeared first on Statistical Modeling,…

## Quickies

February 22, 2014
By

I received a few emails today on bloggable topics. Rather than expanding each response into a full post, I thought I’d just handle them all quickly. 1. Steve Roth asks what I think of this graph: I replied: Interseting but perhaps misleading, as of course any estimate of elasticity of -20 or +5 or whatever […]The post Quickies appeared first on Statistical Modeling, Causal Inference, and Social Science.

## The replication and criticism movement is not about suppressing speculative research; rather, it’s all about enabling science’s fabled self-correcting nature

February 19, 2014
By

Jeff Leek points to a post by Alex Holcombe, who disputes the idea that science is self-correcting. Holcombe writes [scroll down to get to his part]: The pace of scientific production has quickened, and self-correction has suffered. Findings that might correct old results are considered less interesting than results from more original research questions. Potential […]The post The replication and criticism movement is not about suppressing speculative research; rather, it’s…

## Stopping rules and Bayesian analysis

February 13, 2014
By

I happened to receive two questions about stopping rules on the same day. First, from Tom Cunningham: I’ve been arguing with my colleagues about whether the stopping rule is relevant (a presenter disclosed that he went out to collect more data because the first experiment didn’t get significant results) — and I believe you have […]The post Stopping rules and Bayesian analysis appeared first on Statistical Modeling, Causal Inference, and…

## How to think about “identifiability” in Bayesian inference?

February 12, 2014
By

We had some questions on the Stan list regarding identification. The topic arose because people were fitting models with improper posterior distributions, the kind of model where there’s a ridge in the likelihood and the parameters are not otherwise constrained. I tried to help by writing something on Bayesian identifiability for the Stan list. Then […]The post How to think about “identifiability” in Bayesian inference? appeared first on Statistical Modeling,…

## My talks in Bristol this Wed and London this Thurs

February 11, 2014
By

1. Causality and statistical learning (Wed 12 Feb 2014, 16:00, at University of Bristol): Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are […]The post My talks in Bristol this Wed and London this Thurs appeared first on…

## Bootstrap averaging: Examples where it works and where it doesn’t work

February 6, 2014
By
$Bootstrap averaging: Examples where it works and where it doesn’t work$

Aki and I write: The very generality of the boostrap creates both opportunity and peril, allowing researchers to solve otherwise intractable problems but also sometimes leading to an answer with an inappropriately high level of certainty. We demonstrate with two examples from our own research: one problem where bootstrap smoothing was effective and led us […]The post Bootstrap averaging: Examples where it works and where it doesn’t work appeared first…