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21 Cards in this Set
- Front
- Back
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Experiment |
A situation involving chance, like rolling a six-sided die. |
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Outcome |
A possible result of an experiment. |
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Sample Space |
The set of all possible outcome for a particular experiment. |
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Set |
An unordered collection of distinct elemts that do not repeat. |
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Probability |
The likelihood of something happening. Specifically, the ratio of desired outcomes or events to the total number of elements in the sample space. |
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Event |
A particular collection of possible outcomes within a sample space, also written as a set. E= {2, 4, 6} |
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Compliment |
The event that consists of all the ways a particular event will not happen. |
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Basic Rules |
1. No negative probabilities. 2. The probability of something (at least one element in a set) occurring is 100%. 3. The total probability of an event either happening or not happening is 100%. |
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Independent Events |
Events that do not influence each other. One happening does not affect the probability of the other happening. |
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Disjoint / Mutually Exclusive Events |
Events with no common elements, meaning they cannot happen at the same time. An event and its complement are disjoint events. |
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(A∪B) A union B |
The collection of outcomes contained within either event A or B. We write the probability of events A or B happening as P(A∪B). |
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Addition rule for disjoint events |
If A and B are disjoint events, the probability of either of them happening is P(A∪B) = P(A) + P(B). |
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(A∩B) A intersect B |
The collection of outcomes contained within both events A and B. We write the probability of events A and B happening as P(A∩B). |
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Addition Rule |
For any two events, the probability that either or both of them happens is P(A∪B) = P(A) + P(B) - P(A∩B). |
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Marginal Probability |
The probability of a single event, P(A). |
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(A|B) A given B |
The occurence of event A given that event B has already occured. |
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Conditional Probability |
The probability of A happening given B has happened, P(A|B). |
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Joint Probability |
The probability of both A and B happening, P(A∩B). |
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Law of Total Probability (LTP) |
A way of calculating the marginal probability of an event with other joint or conditional probabilities. 1. P(A) = P(A∩B) + P(A∩BC) 2. P(A) = [P(A | B) x P(B)] + [P(A | BC) x P(BC)] |
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Multiplication Rule |
The joint probability of two events is P(A∩B) = P(A | B) x P(B). |
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Bayes Rule |
A formula for finding conditional probabilities: P (A | B) = [P(B | A) x P(A)] ÷ P(B). |
