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Perplex
Perplex
Dashboard
Topics
Exponents & LogarithmsApproximations & ErrorSequences & SeriesCounting & BinomialsProof and Reasoning
Cartesian plane & linesQuadraticsFunction TheoryTransformations & asymptotes
2D & 3D GeometryTrig equations & identities
ProbabilityDescriptive StatisticsBivariate StatisticsDistributions & Random Variables
DifferentiationIntegration
Review VideosFormula BookletMy Progress
BlogLanding Page
Sign UpLogin
Perplex
IB Math AASL
/
Probability
/
Skills
Edit

Skill Checklist

Track your progress across all skills in your objective. Mark your confidence level and identify areas to focus on.

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

IB Math AASL
/
Probability
/
Skills
Edit

Skill Checklist

Track your progress across all skills in your objective. Mark your confidence level and identify areas to focus on.

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Skill Checklist

Track your progress across all skills in your objective. Mark your confidence level and identify areas to focus on.

15 Skills Available

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Probabilistic Events

6 skills
Trial, Outcome and Event
SL 4.5

In probability, a trial is any procedure with an uncertain result, such as flipping a coin, rolling a die, or drawing a card. Each possible result of a trial is called an outcome.


An event is a collection of one or more outcomes, representing scenarios we're interested in, such as rolling an even number or drawing a red card. Events are the probabilities we calculate, and are typically denoted with letters such as ​A, so that the "probability of an event ​A​" is given by ​P(A).

Sample Space
SL 4.5

All possible outcomes from a single trial form the sample space, denoted ​U.


The overall probability of the sample space, denoted ​P(U), is ​1. This expresses the idea that if you perform a trial, something must happen.

Theoretical Probability
SL 4.5

Theoretical probability is calculated based on reasoning or mathematical principles—it's what we expect to happen. When outcomes are equally likely, the probability of an event is given by

​
P(A)=n(U)n(A)​📖
​

where ​n(A)​ is the number of outcomes in event ​A, and ​n(U)​ is the total number of outcomes in the sample space.

Experimental Probability
SL 4.5

Experimental probability (or relative frequency) is found by actually conducting trials and observing outcomes. The relative frequency is calculated by:

​
Relative frequency=total number of trialsnumber of times event occurs​
​


While theoretical probability tells us what's expected, experimental probability tells us what's observed.

Expected number of occurrences
SL 4.5

For an event ​A​ with probability ​P(A), the expected number of occurrences of ​A​ after ​n​ trials is given by

​
Expected number of occurrences of A=P(A)×n
​

This is another way of saying that for every ​n​ trials, ​A​ will happen an average of ​P(A)×n​ times.

Complementary Event
SL 4.5

The complement of an event ​A, denoted ​A′, is the event that ​A​ does not happen. Since ​A​ either happens or it doesn't, then exactly one of ​A​ and ​A′​ must happen for each trial:

​
P(A)+P(A′)=1📖
​

This expresses the idea that the probability of the entire outcome space is ​1.

Combined Events

7 skills
Sample space diagrams
SL 4.6

A sample space diagram is a table representation for scenarios where two probability events occur side by side, and we're interested in certain combinations of those events.


For example, consider a game where a player rolls a die and then flips a coin. If the coin lands on heads, we add ​1​ to their die roll, and if it lands on tails we double their die roll.

​U​

​1​

​2​

​3​

​4​

​5​

​6​

Heads

​1+1=2​

​3​

​4​

​5​

​6​

​7​

Tails

​2×1=2​

​4​

​6​

​8​

​10​

​12​

Then we can answer questions like:

What is the probability of scoring higher than 6?


To do this, we simply count the number of cells that are greater than ​6​ and divide by the total number of cells. So in this case, we have the cells with ​7,8,10,12, so the answer is

​
124​=31​
​
Venn Diagrams
SL 4.6

A Venn diagram is a visual tool used to illustrate relationships between sets or events. Each event is represented by a circle, and overlaps between circles represent shared outcomes. All circles lie within the larger universe  ​U, which is the whole sample space.

Venn diagrams are often filled in with numbers representing the number of samples in each category.

Intersection of probabilities
SL 4.6

The intersection is the event where both ​A​ and ​B​ occur simultaneously, denoted ​A∩B.

Union of probabilities
SL 4.6

The union is the event that at least one of ​A​ or ​B​ occurs. The union is denoted ​A∪B​ and has probability

​
P(A∪B)=P(A)+P(B)−P(A∩B)📖
​

This formula is sometimes referred to as the inclusion-exclusion rule. It is often rearranged in the form

​
P(A∩B)=P(A)+P(B)−P(A∪B)🚫
​
Mutually exclusive events
SL 4.6

Events are mutually exclusive if they cannot both occur at once. In this case, the intersection probability is zero:

​
P(A∩B)=0🚫
​

And therefore

​
P(A∪B)=P(A)+P(B)📖
​
Conditional Probability
SL 4.6

Conditional probability is the probability of event ​A​ happening given we already know event ​B​ has occurred. It's calculated by taking the probability that both events occur, divided by the probability of the known event ​B:

​
P(A∣B)=P(B)P(A∩B)​📖
​


Notice that we can rearrange this formula to get a general formula for the probability of multiple events,

​
P(A∩B)=P(A∣B)P(B)=P(B∣A)P(A)
​
Independent events
SL 4.6

If two events ​A​ and ​B​ are independent, then knowing whether or not one happened gives no information on whether or not the other happened, and

​
P(A∣B)=P(A),P(B∣A)=P(B)
​

Rearranging the conditional probability formula gives the fact that for independent ​A​ and ​B,

​
P(A∩B)=P(A)×P(B)
​

Tree Diagrams

2 skills
Selection with & without replacement
SL 4.6

In probability, a selection is the action of choosing one or more items from a set or group. We can perform selections either with replacement or without replacement.


In selection with replacement, each chosen item is returned to the original group before the next choice, keeping the probabilities constant across selections.


In selection without replacement, the chosen items are removed from the group, causing probabilities to change after each pick because the number of available items decreases.


This difference significantly impacts how probabilities are calculated, especially in problems involving multiple selections.

Tree Diagram
SL 4.6

A tree diagram is a map of what can happen, one step at a time. We use them in probability scenarios with multiple steps, mostly when the result of one step affects the probability of the next step.


To explain, consider this example: We have a bag with ​3​ red marbles and ​2​ green marbles. You draw one marble from the bag, put it in your pocket, and then draw a second marble from the bag.

The diagram starts at the point where nothing has happened yet, and the branches show the different possibilities for what can happen next.


There are two branches leaving from the start: one goes to "G" (green), the other to "R". They are numbered with the probability of the first marble being a certain color. Since there are ​3​ red marbles and a total of ​5​ marbles in the bag, that branch has probability ​53​.


Now the tree has split into two possibilities, and for each of those there are two possibilities for what can happen next. But those probabilities depend on what the first marble was, because if we drew a green marble there is only ​1​ green marble left.

The probabilities on the next four branches are based on where you already are in the tree. For example, the ​41​​ in the top right is the probability that the second marble is green if the first marble was green.


The probability of drawing two green marbles can be found by multiplying the branches that lead from "start" to two greens:

​
P(GG)=52​×41​=101​
​


The probability of drawing two different color marbles (in any order) requires adding the probability of ​RG​ and the probability of ​GR:

​
P(different)=52​×43​+53​×42​=206​+206​=2012​=53​
​

Skill Checklist

Track your progress across all skills in your objective. Mark your confidence level and identify areas to focus on.

15 Skills Available

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Track your progress:

Don't know

Working on it

Confident

📖 = included in formula booklet • 🚫 = not in formula booklet

Probabilistic Events

6 skills
Trial, Outcome and Event
SL 4.5

In probability, a trial is any procedure with an uncertain result, such as flipping a coin, rolling a die, or drawing a card. Each possible result of a trial is called an outcome.


An event is a collection of one or more outcomes, representing scenarios we're interested in, such as rolling an even number or drawing a red card. Events are the probabilities we calculate, and are typically denoted with letters such as ​A, so that the "probability of an event ​A​" is given by ​P(A).

Sample Space
SL 4.5

All possible outcomes from a single trial form the sample space, denoted ​U.


The overall probability of the sample space, denoted ​P(U), is ​1. This expresses the idea that if you perform a trial, something must happen.

Theoretical Probability
SL 4.5

Theoretical probability is calculated based on reasoning or mathematical principles—it's what we expect to happen. When outcomes are equally likely, the probability of an event is given by

​
P(A)=n(U)n(A)​📖
​

where ​n(A)​ is the number of outcomes in event ​A, and ​n(U)​ is the total number of outcomes in the sample space.

Experimental Probability
SL 4.5

Experimental probability (or relative frequency) is found by actually conducting trials and observing outcomes. The relative frequency is calculated by:

​
Relative frequency=total number of trialsnumber of times event occurs​
​


While theoretical probability tells us what's expected, experimental probability tells us what's observed.

Expected number of occurrences
SL 4.5

For an event ​A​ with probability ​P(A), the expected number of occurrences of ​A​ after ​n​ trials is given by

​
Expected number of occurrences of A=P(A)×n
​

This is another way of saying that for every ​n​ trials, ​A​ will happen an average of ​P(A)×n​ times.

Complementary Event
SL 4.5

The complement of an event ​A, denoted ​A′, is the event that ​A​ does not happen. Since ​A​ either happens or it doesn't, then exactly one of ​A​ and ​A′​ must happen for each trial:

​
P(A)+P(A′)=1📖
​

This expresses the idea that the probability of the entire outcome space is ​1.

Combined Events

7 skills
Sample space diagrams
SL 4.6

A sample space diagram is a table representation for scenarios where two probability events occur side by side, and we're interested in certain combinations of those events.


For example, consider a game where a player rolls a die and then flips a coin. If the coin lands on heads, we add ​1​ to their die roll, and if it lands on tails we double their die roll.

​U​

​1​

​2​

​3​

​4​

​5​

​6​

Heads

​1+1=2​

​3​

​4​

​5​

​6​

​7​

Tails

​2×1=2​

​4​

​6​

​8​

​10​

​12​

Then we can answer questions like:

What is the probability of scoring higher than 6?


To do this, we simply count the number of cells that are greater than ​6​ and divide by the total number of cells. So in this case, we have the cells with ​7,8,10,12, so the answer is

​
124​=31​
​
Venn Diagrams
SL 4.6

A Venn diagram is a visual tool used to illustrate relationships between sets or events. Each event is represented by a circle, and overlaps between circles represent shared outcomes. All circles lie within the larger universe  ​U, which is the whole sample space.

Venn diagrams are often filled in with numbers representing the number of samples in each category.

Intersection of probabilities
SL 4.6

The intersection is the event where both ​A​ and ​B​ occur simultaneously, denoted ​A∩B.

Union of probabilities
SL 4.6

The union is the event that at least one of ​A​ or ​B​ occurs. The union is denoted ​A∪B​ and has probability

​
P(A∪B)=P(A)+P(B)−P(A∩B)📖
​

This formula is sometimes referred to as the inclusion-exclusion rule. It is often rearranged in the form

​
P(A∩B)=P(A)+P(B)−P(A∪B)🚫
​
Mutually exclusive events
SL 4.6

Events are mutually exclusive if they cannot both occur at once. In this case, the intersection probability is zero:

​
P(A∩B)=0🚫
​

And therefore

​
P(A∪B)=P(A)+P(B)📖
​
Conditional Probability
SL 4.6

Conditional probability is the probability of event ​A​ happening given we already know event ​B​ has occurred. It's calculated by taking the probability that both events occur, divided by the probability of the known event ​B:

​
P(A∣B)=P(B)P(A∩B)​📖
​


Notice that we can rearrange this formula to get a general formula for the probability of multiple events,

​
P(A∩B)=P(A∣B)P(B)=P(B∣A)P(A)
​
Independent events
SL 4.6

If two events ​A​ and ​B​ are independent, then knowing whether or not one happened gives no information on whether or not the other happened, and

​
P(A∣B)=P(A),P(B∣A)=P(B)
​

Rearranging the conditional probability formula gives the fact that for independent ​A​ and ​B,

​
P(A∩B)=P(A)×P(B)
​

Tree Diagrams

2 skills
Selection with & without replacement
SL 4.6

In probability, a selection is the action of choosing one or more items from a set or group. We can perform selections either with replacement or without replacement.


In selection with replacement, each chosen item is returned to the original group before the next choice, keeping the probabilities constant across selections.


In selection without replacement, the chosen items are removed from the group, causing probabilities to change after each pick because the number of available items decreases.


This difference significantly impacts how probabilities are calculated, especially in problems involving multiple selections.

Tree Diagram
SL 4.6

A tree diagram is a map of what can happen, one step at a time. We use them in probability scenarios with multiple steps, mostly when the result of one step affects the probability of the next step.


To explain, consider this example: We have a bag with ​3​ red marbles and ​2​ green marbles. You draw one marble from the bag, put it in your pocket, and then draw a second marble from the bag.

The diagram starts at the point where nothing has happened yet, and the branches show the different possibilities for what can happen next.


There are two branches leaving from the start: one goes to "G" (green), the other to "R". They are numbered with the probability of the first marble being a certain color. Since there are ​3​ red marbles and a total of ​5​ marbles in the bag, that branch has probability ​53​.


Now the tree has split into two possibilities, and for each of those there are two possibilities for what can happen next. But those probabilities depend on what the first marble was, because if we drew a green marble there is only ​1​ green marble left.

The probabilities on the next four branches are based on where you already are in the tree. For example, the ​41​​ in the top right is the probability that the second marble is green if the first marble was green.


The probability of drawing two green marbles can be found by multiplying the branches that lead from "start" to two greens:

​
P(GG)=52​×41​=101​
​


The probability of drawing two different color marbles (in any order) requires adding the probability of ​RG​ and the probability of ​GR:

​
P(different)=52​×43​+53​×42​=206​+206​=2012​=53​
​