Finding an Unfair Coin with R

Sebastian Sauer works out a coin flip problem:

A stochastic problem, with application to financial theory. Some say it goes back to Warren Buffett. I relied to my colleague Norman Markgraf, who pointed it out to me.

Assume there are two coins. One is fair, one is loaded. The loaded coin has a bias of 60-40. Now, the question is: How many coin flips do you need to be “sure enough” (say, 95%) that you found the loaded coin?

Let’s simulate la chose.

It took a few more flips than I had expected but the number is not outlandish.

Related Posts

MAPE and Its Flaws

Jan Fischer takes us through Mean Absolute Percentage Error as a measure of forecast quality: Particular small actual values bias the MAPE.If any true values are very close to zero, the corresponding absolute percentage errors will be extremely high and therefore bias the informativity of the MAPE (Hyndman & Koehler 2006). The following graph clarifies this […]

Read More

From Excel to R: Three Examples

Abdul Majed Raja has a few examples of things which are easy to do in Excel and how you can do them in R: Create a difference variable between the current value and the next valueThis is also known as lead and lag – especially in a time series dataset this varaible becomes very important in feature engineering. In […]

Read More


April 2019
« Mar May »