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

Posted by Andrew on 8 October 2018, 7:59 pm

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

## Recent Comments

- Ed Hagen on “Superior: The Return of Race Science,” by Angela Saini
- Carlos Ungil on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Terry on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Terry on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Keith O'Rourke on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Joshua B. Miller on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- David J. Littleboy on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- D Kane on “Superior: The Return of Race Science,” by Angela Saini
- Terry on Deterministic thinking (“dichotomania”): a problem in how we think, not just in how we act
- Terry on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Martha (Smith) on Deterministic thinking (“dichotomania”): a problem in how we think, not just in how we act
- Andrew on “Suppose that you work in a restaurant…”
- Nick Adams on “Suppose that you work in a restaurant…”
- Steve on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Joshua B. MIller on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Dzhaughn on “Suppose that you work in a restaurant…”
- AllanC on “Suppose that you work in a restaurant…”
- Joe on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem
- Keith O'Rourke on “Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science”
- Carlos Ungil on Josh Miller’s alternative, more intuitive, formulation of Monty Hall problem

## Categories

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)

“Transcript is here”

Found the audio but the Transcript was unavailable.