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Breaking a Stick Into a Triangle

Break a stick at two random points into three pieces, what's the chance they form a triangle? The puzzle rewards translating a geometry question into a picture of the sample space itself.

Prerequisites: Choosing Coordinates That Kill the Problem

Take a stick of length 1. Pick two points on it uniformly at random and break the stick at both, you now have three pieces. What's the probability that those three pieces can be arranged into a triangle?

Try it before reading on. The instinct is to guess something close to a half, or to reach for calculus. Neither is needed, the whole problem collapses once you stop thinking about "pieces of a stick" and start thinking about a single point on a two-dimensional map.

Setting up the picture

Call the two break points xx and yy, both drawn uniformly from [0,1][0,1]. Every possible pair (x,y)(x, y) is a point inside the unit square, and because xx and yy are independent and uniform, that square is the sample space, every point in it is equally likely, so a probability is just an area.

Assume for a moment x<yx < y (you'll double back for the symmetric case). Then the three pieces have lengths xx, yxy - x, and 1y1 - y. Three segments form a triangle exactly when no one piece is longer than the sum of the other two, equivalently, no piece exceeds half the total length. With total length 1, that means all three pieces must be shorter than 12\tfrac{1}{2}.

Turning "can these three lengths form a triangle" into "is this point inside a region of the square" is the whole trick. Once the condition on xx and yy is written down, the probability is just (area of the good region) ÷ (area of the square).

Working the three inequalities

Write out the three no-piece-too-long conditions for x<yx < y:

x<12,yx<12,1y<12x < \frac{1}{2}, \qquad y - x < \frac{1}{2}, \qquad 1 - y < \frac{1}{2}

The first says the left piece is short. The third rearranges to y>12y > \tfrac{1}{2}: the right piece is short. The middle one, y<x+12y < x + \tfrac{1}{2}, says the middle piece is short.

Plot these three lines inside the triangle 0<x<y<10 < x < y < 1 (half the unit square, since we assumed x<yx<y). Shading the region where all three hold carves out a smaller triangle in the middle of that half-square. Working the vertices out, the lines x=12x=\tfrac12, y=12y=\tfrac12, and y=x+12y = x+\tfrac12 meet the boundary x<y<1x<y<1 at (0,12)(0,\tfrac12), (12,12)(\tfrac12,\tfrac12), and (12,1)(\tfrac12,1), that inner triangle has area exactly 14\tfrac{1}{4} of the area of the x<yx<y half-triangle (which itself has area 12\tfrac12), so its area relative to the full unit square is 18\tfrac18.

Doubling for the symmetric case y<xy < x gives another region of area 18\tfrac{1}{8}, for a total good area of 14\tfrac{1}{4} out of the full square's area of 1.

P(triangle)=14P(\text{triangle}) = \frac{1}{4}

Only one time in four does a randomly broken stick make a triangle, most breaks produce one piece that's simply too long relative to the other two, because a uniform break point has a real chance of landing very close to one end.

0 1 0 1 y x x = y
The shaded diamond, one quarter of the unit square's area, is exactly the set of break points that form a triangle.

The transferable technique

Whenever a puzzle involves several independent uniform random quantities and a geometric condition on them (triangle formation, whether points fall within a distance, whether a sum stays under a bound), the move is the same: let the variables be coordinates, draw the region satisfying the condition, and read off a probability as a ratio of areas or volumes. This turns algebra-heavy conditional reasoning into a single picture, and it's the standard opening move for the whole family of stick-breaking, meeting-time, and random-point puzzles that show up in quant interviews.

When a puzzle gives you two or more independent uniform variables, sketch the square (or cube) immediately, most of these problems are solved the moment the picture is right, well before any integral is needed.

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Practice in interviews

Further reading

  • Mosteller, Fifty Challenging Problems in Probability
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