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Question

A bag has 4 fair coins (1 head 1 tail) and 4 double-headed coins. A coin is drawn and flipped 3 times all heads. Probability it's double-headed?

This question was previously asked in
SSC Stenographer 2025 Question Paper (06-Aug-2025) Shift 2
The correct answer is

8/9 

This problem involves calculating a conditional probability, specifically finding the probability that the selected coin is double-headed given that it landed heads three times in a row. We can solve this using Bayes' Theorem.

Defining Events

  • Let DH be the event that a double-headed coin is selected.
  • Let F be the event that a fair coin is selected.
  • Let E be the event that the coin is flipped 3 times and results in 3 heads (HHH).

Prior Probabilities

There are 8 coins in total (4 fair + 4 double-headed).

  • The probability of selecting a double-headed coin is $P(\text{DH}) = \frac{4}{8} = \frac{1}{2}$.
  • The probability of selecting a fair coin is $P(\text{F}) = \frac{4}{8} = \frac{1}{2}$.

Likelihood of the Evidence

We need to find the probability of observing 3 heads (E) given the type of coin selected.

  • Probability of E given a double-headed coin (DH): A double-headed coin always lands heads. So, the probability of getting 3 heads in a row is: $P(\text{E} | \text{DH}) = 1 \times 1 \times 1 = 1$
  • Probability of E given a fair coin (F): A fair coin has a $1/2$ probability of landing heads on each flip. So, the probability of getting 3 heads in a row is: $P(\text{E} | \text{F}) = \left(\frac{1}{2}\right) \times \left(\frac{1}{2}\right) \times \left(\frac{1}{2}\right) = \left(\frac{1}{2}\right)^3 = \frac{1}{8}$

Calculating the Total Probability of the Evidence (P(E))

We use the law of total probability to find the overall probability of observing 3 heads:

$P(\text{E}) = P(\text{E} | \text{DH}) P(\text{DH}) + P(\text{E} | \text{F}) P(\text{F})$ $P(\text{E}) = \left(1 \times \frac{1}{2}\right) + \left(\frac{1}{8} \times \frac{1}{2}\right)$ $P(\text{E}) = \frac{1}{2} + \frac{1}{16}$ $P(\text{E}) = \frac{8}{16} + \frac{1}{16} = \frac{9}{16}$

Applying Bayes' Theorem

Now we apply Bayes' Theorem to find the probability that the coin was double-headed given the evidence of 3 heads:

$P(\text{DH} | \text{E}) = \frac{P(\text{E} | \text{DH}) P(\text{DH})}{P(\text{E})}$ $P(\text{DH} | \text{E}) = \frac{1 \times \frac{1}{2}}{\frac{9}{16}}$ $P(\text{DH} | \text{E}) = \frac{\frac{1}{2}}{\frac{9}{16}}$ $P(\text{DH} | \text{E}) = \frac{1}{2} \times \frac{16}{9}$ $P(\text{DH} | \text{E}) = \frac{16}{18}$ $P(\text{DH} | \text{E}) = \frac{8}{9}$

Therefore, the probability that the coin drawn was double-headed, given that it landed heads 3 times, is $8/9$.

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