Let a sample space be . Which of the following assignments of probabilities to each outcome are valid?
| Outcomes | ||||||
|---|---|---|---|---|---|---|
| (a) | ||||||
| (b) | ||||||
| (c) | ||||||
| (d) | ||||||
| (e) |
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Start your 14-day free trial to unlock the full solution →A probability assignment is valid only if every probability is between 0 and 1 (inclusive) and the sum of all probabilities equals exactly 1. Checking these two conditions shows that only assignments (a) and (b) are valid.
The core idea is simple: probability is a measure of how likely an outcome is. The axioms of probability (Kolmogorov’s axioms) give us two non-negotiable rules for any finite sample space:
- Non-negativity: Every outcome’s probability must be .
- Normalization: The sum of probabilities over all outcomes must equal exactly .
That’s it. No probability can be negative, none can exceed 1 (though that’s actually a consequence of the sum being 1 and non-negativity — if one probability were >1, the sum would exceed 1 unless others were negative, which is forbidden). So we just test each table row against these two rules.
Let’s go through each option.
-
Option (a): Each gets .
- All values are positive, so non-negativity holds.
- Sum = .
- Both conditions satisfied. Valid.
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Option (b): , all others .
- All probabilities are .
- Sum = .
- Valid. This is a degenerate (but perfectly legal) distribution where only one outcome ever occurs.
-
Option (c): Values are .
- Immediately, and have negative probabilities. That violates non-negativity.
- Even if we ignored that, the sum would be . Let’s compute: , and . So total = .
- Fails both conditions. Invalid.
-
Option (d): Values: .
- All are non-negative, so that’s fine. …
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