The volume and frequency of opinion polls in the UK has increased enormously since the advent of online polling in the early 2000s, bringing with it heightened scrutiny of potential changes in public sentiment on political and social issues. Polls measuring vote intention are now published on an almost weekly […] Read more »
About that claim in the NYT that the immigration issue helped Hillary Clinton? The numbers don’t seem to add up.
Today I noticed an op-ed by two political scientists, Howard Lavine and Wendy Rahm, entitled, “What if Trump’s Nativism Actually Hurts Him?” … Now I was curious, so I thought I’d check the numbers. CONT. Andrew Gelman (Columbia U.), Statistical Modeling, Causal Inference, and Social Science Read more »
Two Ways of Thinking About Election Predictions and What They Tell Us About 2018
To understand the differences between quantitative, data-driven predictions and those made from traditional, data-influenced handicapping, one should direct their attention to the names of two websites: Sabato’s Crystal Ball at the University of Virginia Center for Politics, and my blog, The Crosstab. One is a reference to the soothsayer, a […] Read more »
Truth, significance & probability in polling
Margin of error is simultaneously the most under- and most over-used concept in polling, in part because so many people don’t really understand it. A Democratic gubernatorial primary survey in Maryland provides an opportunity to re-engage with these issues. CONT. Mark Mellman (Mellman Group), The Hill Read more »
How surprising was Trump’s victory? Evaluations of the 2016 U.S. presidential election and a new poll aggregation model
The U.S. presidential election results of 2016 surprised many poll-watchers, suggesting possible biases in estimated support for the major party candidates and posing a challenge for poll aggregation as a prediction tool. Using data from earlier elections and the 2016 campaign, we evaluated poll aggregation performance for the major prediction […] Read more »
Book Review: ‘News, Numbers and Public Opinion in a Data-Driven World’
In 1954, Darrell Huff published How to Lie with Statistics, a tongue-in-cheek guide for those wanting to use numbers to deceive. The book, now widely distributed to first-year university statistics students, outlines how statistics can confuse and muddle both writers and readers. The upshot? Huff suggests that: ‘without writers who […] Read more »