Bertrand's Box Paradox Calculator

Bertrand's Box Paradox

Calculate conditional probability using Bayes' Theorem. Explore how evidence (drawing a gold coin) updates the probability of which box you selected.

Last updated: March 2026

Enter Box Counts

P(other coin is gold | drew gold)
66.67%
= 2 / 3

Posterior Probability by Box Distribution

GG BoxesGS BoxesSS BoxesP(Other Gold)
11166.67% (2/3)
21180.00% (4/5)
12150.00% (2/4)
101100% (2/2) ← Certain
0110% (0/1) ← Impossible

What is Bertrand's Box Paradox?

Bertrand's Box Paradox is a classic probability problem that demonstrates the importance of careful conditional reasoning and Bayesian inference. The paradox often surprises people because the intuitive answer differs significantly from the correct mathematical result.

The Setup: You have three indistinguishable boxes. One contains two gold coins (GG), one contains one gold and one silver coin (GS), and one contains two silver coins (SS). You randomly select a box and draw one coin from it—the coin is gold. What is the probability that the remaining coin in the box is also gold?

The key insight is that drawing a gold coin provides strong evidence about which box you selected. The GG box is twice as likely to produce a gold coin as the GS box (2 gold coins vs. 1 gold coin). This asymmetry is what makes the posterior probability 2/3, not 1/2.

How to Use This Calculator

1

Enter Box Counts

Specify how many boxes of each type (GG, GS, SS) are in your scenario. Start with 1 of each for the classic problem.

2

View Posterior Probability

The calculator uses Bayes' Theorem to compute P(other coin is gold | drew gold). This accounts for the different probabilities of drawing gold from each box type.

3

Experiment with Variations

Try changing the box counts to see how the posterior probability changes. More GG boxes increase the probability; more GS boxes decrease it.

Formula:

P(GG | gold) = (2·GG) / (2·GG + GS)

Worked Example

Why it's not 50-50 (the classic error):

Setup:
1 GG box, 1 GS box, 1 SS box. You randomly select and draw gold.
Naive Think:
"Gold coin ⟹ must be GG or GS box"
"Two possibilities, equally likely ⟹ P = 50%"
❌ WRONG: Ignores asymmetry
Correct Math:
Gold can come from: GG (2 ways) or GS (1 way)
Total ways to draw gold = 2 + 1 = 3
Ways that mean GG = 2
P(GG | gold) = 2/3 ≈ 66.67% ✓
Key Insight: GG is twice as likely to produce gold because it has twice as many gold coins.

Frequently Asked Questions

Why isn't the answer 50%?

Intuition says two equally likely boxes (GG or GS), so 50%. But GG produces gold twice as often, making it twice as likely given that we drew gold.

How does this relate to Bayes' Theorem?

We update our belief about which box was selected (prior: 1/3 each) using evidence that gold was drawn (likelihood differs by box type).

What if I change the box counts?

The formula adjusts automatically. More GG boxes increase probability; more GS boxes decrease it; SS boxes are irrelevant (can't produce gold).

Is this related to the Monty Hall Problem?

Both involve conditional probability and how new information changes probabilities. Both confound intuition in similar ways.

What does 'other coin is also gold' mean?

After drawing one coin from the box (which is gold), one coin remains inside. We ask: what's the probability that remaining coin is gold?

Can the probability exceed 100%?

No, the result is always 0–100%. Maximum (100%) occurs with only GG boxes; minimum (0%) occurs when GG = 0 and GS > 0.

Why is it called a 'paradox'?

It's paradoxical because outcomes seem equally likely at first glance, but careful analysis reveals asymmetry. The result contradicts naive intuition.

What if all three types have equal probabilities?

With n GG, n GS, n SS boxes: P(other gold) = 2/(2+1) = 2/3 regardless of n. This is Bertrand's original problem.

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