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The problem asks for the missing probability under the category "Other" in the table. The sum of all probabilities in a probability distribution must equal 1. 

From the table, we are given:
- Probability for English: 0.57
- Probability for French: 0.21
- Probability for Other: ?

Let the probability for "Other" be \( P(\text{Other}) \).

To find this missing probability, we use the fact that the sum of the probabilities must equal 1:

\[
P(\text{English}) + P(\text{French}) + P(\text{Other}) = 1
\]

Substituting the known values:

\[
0.57 + 0.21 + P(\text{Other}) = 1
\]

\[
0.78 + P(\text{Other}) = 1
\]

\[
P(\text{Other}) = 1 - 0.78 = 0.22
\]

Thus, the probability for "Other" is \( 0.22 \).

Would you like further details or have any other questions? Here are 5 related questions to deepen your understanding:

1. What are the characteristics of a probability distribution?
2. How would you verify that a distribution table is valid?
3. If one of the given probabilities was unknown, how would you calculate it?
4. Why do the probabilities in a distribution sum to 1?
5. What real-world applications might require the use of probability distributions?

**Tip:** Always check that all the values in a probability distribution add up to exactly 1.

What probability should replace '?' in the distribution?

Learn how to find the missing probability in a distribution table where the sum of all probabilities must equal 1. This example focuses on a table with English, French, and 'Other' categories, with detailed steps to calculate the probability for 'Other'.

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