The following problem appeared in Volume 97, Issue 3 (2024) of Mathematics Magazine.
Two points
and
are chosen at random (uniformly) from the interior of a unit circle. What is the probability that the circle whose diameter is segment
lies entirely in the interior of the unit circle?
As discussed in the previous post, I guessed from simulation that the answer is . Naturally, simulation is not a proof, and so I started thinking about how to prove this.
My first thought was to make the problem simpler by letting only one point be chosen at random instead of two. Suppose that the point is fixed at a distance
from the origin. What is the probability that the point
, chosen at random, uniformly, from the interior of the unit circle, has the desired property?
My second thought is that, by radial symmetry, I could rotate the figure so that the point is located at
. In this way, the probability in question is ultimately going to be a function of
.
There is a very nice way to compute such probabilities since is chosen at uniformly from the unit circle. Let
be the probability that the point
has the desired property. Since the area of the unit circle is
, the probability of desired property happening is
.
So, if I could figure out the shape of , I could compute this conditional probability given the location of the point
.
But, once again, I initially had no idea of what this shape would look like. So, once again, I turned to simulation with Mathematica.
First, a technical detail that I ignored in the previous post. To generate points at random inside the unit circle, one might think to let
and
, where the distance from the origin
is chosen at random between 0 and 1 and the angle
is chosen at random from
. Unfortunately, this simple simulation generates too many points that are close to the origin and not enough that are close to the circle:

To see why this happened, let denote the distance of a randomly chosen point from the origin. Then the event
is the same as saying that the point lies inside the circle centered at the origin with radius
, so that the probability of this event should be
.
However, in the above simulation, was chosen uniformly from
, so that
. All this to say, the above simulation did not produce points uniformly chosen from the unit circle.
To remedy this, we employ the standard technique of using the inverse of the above function , which is clearly
. In other words, we will chose randomly chosen radius to have the form
, where
is chosen uniformly on
. In this way,
,
as required. Making this modification (highlighted in yellow) produces points that are more evenly distributed in the unit circle; any bunching of points or empty spaces are simply due to the luck of the draw.

In the next post, I’ll turn to the simulation of .








