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Which situation represents theoretical probability?
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GPT-5 mini
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Theoretical probability is when you can determine probabilities from known, equally likely outcomes (no experiments needed) — probability = number of favorable outcomes ÷ total number of equally likely outcomes.
Examples that represent theoretical probability
- Rolling a fair six-sided die: P(rolling a 3) = 1/6.
- Drawing one card from a well-shuffled standard deck: P(drawing an ace) = 4/52 = 1/13.
- Spinning a fair spinner with 8 equal sectors: P(landing on blue) = (number of blue sectors)/8.
Non-examples (experimental/empirical probability)
- Flipping a coin 100 times and getting 60 heads (this is describing experimental probability 60/100).
- Survey results, observed frequencies, or biased/unknown-outcome devices.
If you have specific answer choices, paste them and I’ll tell you which one is theoretical.
Examples that represent theoretical probability
- Rolling a fair six-sided die: P(rolling a 3) = 1/6.
- Drawing one card from a well-shuffled standard deck: P(drawing an ace) = 4/52 = 1/13.
- Spinning a fair spinner with 8 equal sectors: P(landing on blue) = (number of blue sectors)/8.
Non-examples (experimental/empirical probability)
- Flipping a coin 100 times and getting 60 heads (this is describing experimental probability 60/100).
- Survey results, observed frequencies, or biased/unknown-outcome devices.
If you have specific answer choices, paste them and I’ll tell you which one is theoretical.
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