## Podcast interview on polling (mostly), also some Bayesian stuff

Hugo Bowne-Anderson interviewed me for a DataCamp podcast. Transcript is here.

1. Jeff says:

A good read. One note: According to Nate Silver’s 2016 post-mortem, the adoption of odds (e.g. 2 in 5) over percentages for forecasting election outcomes seems to be more about avoiding a specific misperception than backing away from unrealistic precision.

“Also, both probabilities and polls are usually listed as percentages, so people can confuse one for the other — they might mistake a forecast showing Clinton with a 70 percent chance of winning as meaning she has a 70-30 polling lead over Trump, which would put her on her way to a historic, 40-point blowout. [in note:] For this reason, we may experiment with listing probabilities as odds — e.g., Trump has a 2 in 7 chance — rather than as percentages in future election years.” https://fivethirtyeight.com/features/the-media-has-a-probability-problem/

I remember seeing this when it was published because I’m interested in the usability of charts and quantitative measures. As of this writing, Democrats have a 20.8% chance of winning control of the Senate but the headline above that number says “1 in 5.”

• 2 in 7 is not odds, it’s probability expressed as a rational number, 2/7 the use of the word “in” clearly expresses the idea that 2 is the number of “successful possibilities” whereas 7 is the “total number of possibilities” so that 2 in 7 represents the ratio of successes to the total, or probability.

odds would be 2 to 5 against, there are 2 successes and 5 non-successes being considered, the probability is 2/(2+5) and the second number doesn’t express the totality of options (in 7) but rather the number of alternative outcomes (to 5)

2. Paul Alper says:

“Transcript is here”

Found the audio but the Transcript was unavailable.