By the end of this chapter you should be familiar with:
Experiment: A process by which you obtain an observation.
Trials: Repeating an experiment a number of times.
Outcome: A possible result of an experiment.
Event: An outcome or set of outcomes.
Sample space: The set of all possible outcomes of an experiment, always denoted by U.
If we have an event A and n trials then: Relative frequency of A = (š¹šššš¢šššš¦ šš šššš¢ššššš šš šš£ššš” š“ šš š š”ššššš ) /š
This is known as experimental probability of the event A.
The formula for theoretical probability P(A) of an event A is: P(A) =š(š“)/š(š)Ā where n(A) is the number of outcomes that make A happen and n(U) is the total number of outcomes.
Probability values can only be between 0 and 1 i.e., 0 ā¤P(A) ⤠1.
Example: What is the probability of rolling a 4 with a die.
Solution: Number of ways it can happen: 1Ā (there is only 1 face with a ā4ā on it)
Total number of outcomes: 6Ā (there are 6 faces altogether)
So, the probability =Ā 1/6
Example: There are 5 marbles in a bag: 4 are blue, and 1 is red. What is the probability that a blue marble gets picked?
Solution: Number of ways it can happen: 4Ā (there are 4 blues)
Total number of outcomes: 5Ā (there are 5 marbles in total)
So, the probability =Ā 4/5Ā = 0.8
REPRESENTING THE SAMPLE SPACE
VENN DIAGRAMS
A venn diagram represents mathematical or logical sets pictorially as circles or closed curves within an enclosing rectangle (the universal set), common elements of the sets being represented by intersections of the circles.
If we have two events A, B:
If two events are mutually exclusive then P(A ā© B) = 0
P(A U B) = P(A) + P(B)
The following table shows how to represent them in the form of a venn diagram:
Example: Draw a Venn diagram that shows the following sample space and events:
Solution: The sample space contains all the positive integers up toĀ 30 S = {1, 2, 3,ā¦,30}
The prime numbers betweenĀ 1Ā andĀ 30 are P = {2, 3, 5, 7, 11, 13, 17, 19, 23, 29}
The multiples ofĀ 3Ā betweenĀ 1Ā andĀ 30Ā are M = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}
The factors ofĀ 30Ā are F = {1, 2, 3, 5, 6, 10, 15, 30}
TREE DIAGRAMS
To solve problems involving independent events, it is often helpful to draw a tree diagram.
Here is a tree diagram for the toss of a coin:
Here are two ābranchesā (Heads and Tails)
The probability of each branch is written on the branch and the outcome is written at the end of the branch.
WeĀ multiplyĀ probabilitiesĀ along the branches and we can alsoĀ addĀ probabilities downĀ columns.
Example: You are off to soccer, and love being the Goalkeeper, but that depends who is the Coach today: With Coach Sam the probability of being Goalkeeper isĀ 0.5 and with Coach Alex the probability of being Goalkeeper isĀ 0.3. Sam is Coach more often, about 6 out of every 10 games. So, what is the probability you will be a Goalkeeper today?
Solution:
Sample space is a term used in mathematics to meanĀ all possible outcomes. For example, the sample space for rolling a normal dice is {1,2,3,4,5,6} as these are all the only outcomes we can obtain.
The sample space for flipping a coin is {H, T}.
What if we wanted to know the possible outcomes for flipping a coinĀ andĀ rolling a dice? The sample space for these two combined events is {H1, H2, H3, H4, H5, H6, T1, T2, T3, T4, T5, T6}.
We could also write out the sample space for rolling two dice, but to simplify things mathematicians often useĀ sample space diagrams.
Look at this sample space diagram for rolling two dice:
From the diagram, we can see that there are 36 possible outcomes. The probability of getting the outcome 3,2 isĀ 1/36Ā because 3,2 only appears once in the sample space diagram and there are 36 outcomes in total.
The probability of getting a 3 and a 2 isĀ 2/36 = 1/18. This is because to get a 3 and a 2 we can have either the outcome 3,2 or the outcome 2,3.
CONDITIONAL PROBABILITY
TheĀ conditional probability (which is basically Bayesā theorem)Ā of an eventĀ BĀ is the probability that the event will occur given the knowledge that an eventĀ AĀ has already occurred. This probability is writtenĀ P(B|A), notation for theĀ probability of B given A. In the case where eventsĀ AĀ andĀ BĀ areĀ independentĀ (where eventĀ AĀ has no effect on the probability of eventĀ B), the conditional probability of eventĀ BĀ given eventĀ AĀ is simply the probability of eventĀ B, that isĀ P(B).
If eventsĀ AĀ andĀ BĀ are not independent, then the probability of theĀ intersection of A and BĀ (the probability that both events occur) is defined by
P(A and B) = P(A)P(B|A).
From this definition, the conditional probabilityĀ P(B|A)Ā is easily obtained by dividing byĀ P(A): P(B|A) = š·(šØā©š©) /š·(šØ)
For independent events,
P(Aā©B) = P(B|A) P(A) = P(A) P(B)
Example: A box contains 14 dark and 6 milk chocolates. Sarah randomly selects three chocolates to eat. Find the probability that Sarah selects:
Solution: