Given that \( P(X) \) is the probability of event \( X \), you want to calculate:

\[
1 - P(X)
\]

From the previous calculation, we found:

\[
P(X) = 0.3
\]

So,

\[
1 - P(X) = 1 - 0.3 = 0.7
\]

This result, \( 1 - P(X) \), is actually the probability of the complementary event, which is the event "not \( X \)". Hence,

\[
1 - P(X) = P(\text{not } X)
\]

Therefore:

\[
1 - P(X) = 0.7
\]

Would you like to go into more detail on this? Here are some related questions that might help:

1. What is the relationship between \( P(X) \) and \( P(\text{not } X) \)?
2. How do complementary probabilities work in mutually exclusive events?
3. How would you express the sum of \( P(X) \) and \( P(\text{not } X) \)?
4. If you knew \( P(\text{not } X) \) first, how would you find \( P(X) \)?
5. How do you interpret \( 1 - P(X) \) in a real-world context?
6. What is the significance of \( P(X) + P(\text{not } X) = 1 \) in probability theory?
7. Can you explain why \( P(\text{not } X) = 1 - P(X) \) must always hold true?
8. How would the calculation change if \( P(X) \) was dependent on another event?

**Tip:** Always remember that the sum of the probabilities of an event and its complement equals 1.

Understand how to calculate the complementary probability 1 - P(X) using probability theory. This problem involves finding the probability of the event 'not X' given the probability of event X. Learn the relationship between these probabilities and their applications. Suitable for advanced learners in probability theory.

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