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IB DP Maths: AI SL

Revision Notes

Home / DP / Maths: AI SL / IB / Revision Notes / 4. Statistics & Probability / 4.3 Probability / 4.3.1 Probability & Types of Events


4.3.1 Probability & Types of Events


Probability Basics

What key words and terminology are used with probability?

  • An experiment is a repeatable activity that has a result that can be observed or recorded
    • Trials are what we call the repeats of the experiment
  • An outcome is a possible result of a trial
  • An event is an outcome or a collection of outcomes
    • Events are usually denoted with capital letters: A, B, etc
    • n(A) is the number of outcomes that are included in event A
    • An event can have one or more than one outcome
  • A sample space is the set of all possible outcomes of an experiment
    • This is denoted by U
    • n(U) is the total number of outcomes
    • It can be represented as a list or a table

How do I calculate basic probabilities?

  • If all outcomes are equally likely then probability for each outcome is the same
    • Probability for each outcome is fraction numerator 1 over denominator n left parenthesis U right parenthesis end fraction
  • Theoretical probability of an event can be calculated without using an experiment by dividing the number of outcomes of that event by the total number of outcomes

begin mathsize 22px style P left parenthesis A right parenthesis equals fraction numerator n left parenthesis A right parenthesis over denominator n left parenthesis U right parenthesis end fraction end style 

    • This is given in the formula booklet
  • Experimental probability (also known as relative frequency) of an outcome can be calculated using results from an experiment by dividing its frequency by the number of trials
    • Relative frequency of an outcome is fraction numerator Frequency blank of blank that space outcome space from blank the blank trials over denominator Total blank number blank of blank trials blank left parenthesis n right parenthesis end fraction

How do I calculate the expected number of occurrences of an outcome?

  • Theoretical probability can be used to calculate the expected number of occurrences of an outcome from n trials
  • If the probability of an outcome is p and there are n trials then:
    • The expected number of occurrences is np
    • This does not mean that there will exactly np occurrences
    • If the experiment is repeated multiple times then we expect the number of occurrences to average out to be np

What is the complement of an event?

  • The probabilities of all the outcomes add up to 1
  • Complementary events are when there are two events and exactly one of them will occur
    • One event has to occur but both events can not occur at the same time
  • The complement of event A is the event where event A does not happen
    • This can be thought of as not A
    • This is denoted A'

begin mathsize 22px style straight P left parenthesis A right parenthesis plus straight P left parenthesis A apostrophe right parenthesis equals 1 end style 

      • This is in the formula booklet
      • It is commonly written as straight P left parenthesis A apostrophe right parenthesis equals 1 minus straight P left parenthesis A right parenthesis

Worked Example

Dave has two fair spinners, A and B. Spinner A has three sides numbered 1, 4, 9 and spinner B has four sides numbered 2, 3, 5, 7. Dave spins both spinners and forms a two-digit number by using the spinner A for the first digit and spinner B for the second digit. 

T is the event that the two-digit number is a multiple of 3.

a)
List all the possible two-digit numbers.

4-3-1-ib-ai-aa-sl-prob-basics-a-we-solution

b)
Find straight P left parenthesis T right parenthesis.

4-3-1-ib-ai-aa-sl-prob-basics-b-we-solution

c)
Find straight P left parenthesis T apostrophe right parenthesis.

4-3-1-ib-ai-aa-sl-prob-basics-c-we-solution

Independent & Mutually Exclusive Events

What are different types of combined events?

  • The intersection of two events (A and B) is the event where both A and B occur
    • This can be thought of as A and B
    • This is denoted as A intersection B
  • The union of two events (A and B) is the event where A or B or both occur
    • This can be thought of as A or B
    • This is denoted A union B
  • The event where A occurs given that event B has occurred is called conditional probability
    • This can be thought as A given B
    • This is denoted A vertical line B

What are mutually exclusive events?

  • Two events are mutually exclusive if they can not both happen at once
    • For example: when rolling a dice the events “getting a prime number” and “getting a 6” are mutually exclusive
  • If A and B are mutually exclusive events then:
    • straight P left parenthesis A intersection B right parenthesis equals 0
  • Complementary events are mutually exclusive

What are independent events?

  • Two events are independent if one occurring does not affect the probability of the other occurring
    • For example: when flipping a coin twice the events “getting a tails on the first flip” and “getting a tails on the second flip” are independent
  • If A and B are independent events then:
    • straight P left parenthesis A vertical line B right parenthesis equals straight P left parenthesis A right parenthesis and straight P left parenthesis B vertical line A right parenthesis equals straight P left parenthesis B right parenthesis
  • If A and B are independent events then:
    • straight P left parenthesis A intersection B right parenthesis equals straight P left parenthesis A right parenthesis straight P left parenthesis B right parenthesis 
      • This is given in the formula booklet

How do I find the probability of combined events?

  • The probability of A or B (or both) occurring can be found using the formula

begin mathsize 22px style straight P open parentheses A union B close parentheses equals straight P open parentheses A close parentheses plus straight P open parentheses B close parentheses minus straight P open parentheses A intersection B close parentheses end style 

      • This is given in the formula booklet
      • You subtract the probability of A and B both occurring because it has been included twice (once in P(A) and once in P(B) )
  • If A and B are mutually exclusive events then

begin mathsize 22px style straight P open parentheses A union B close parentheses equals straight P open parentheses A close parentheses plus straight P open parentheses B close parentheses end style

      • This is given in the formula booklet
      • This occurs because straight P open parentheses A intersection B close parentheses equals 0
  • For any two events A and B the events A intersection B and A intersection B apostrophe are mutually exclusive and A is the union of these two events
    • straight P left parenthesis A right parenthesis equals straight P left parenthesis A intersection B right parenthesis plus straight P left parenthesis A intersection B apostrophe right parenthesis
      • This works for any two events A and B

Worked Example

a)
A student is chosen at random from a class. The probability that they have a dog is 0.8, the probability they have a cat is 0.6 and the probability that they have a cat or a dog is 0.9.
Find the probability that the student has both a dog and a cat.

4-3-1-ib-ai-aa-sl-types-of-events-a-we-solution

b)
Two events, Q and R, are such that straight P left parenthesis Q right parenthesis equals 0.8 and straight P left parenthesis Q intersection R right parenthesis equals 0.1.
Given that Q and R are independent, find straight P left parenthesis R right parenthesis.

4-3-1-ib-ai-aa-sl-types-of-events-b-we-solution

c)
Two events, S and T, are such that straight P left parenthesis S right parenthesis equals 2 straight P left parenthesis T right parenthesis.
Given that S and T are mutually exclusive and that straight P left parenthesis S union T right parenthesis equals 0.6 find straight P left parenthesis S right parenthesis and straight P left parenthesis T right parenthesis.

4-3-1-ib-ai-aa-sl-types-of-events-c-we-solution



  • 1. Number & Algebra
    • 1.1 Number Toolkit
      • 1.1.1 Standard Form
        • 1.1.2 Exponents & Logarithms
          • 1.1.3 Approximation & Estimation
            • 1.1.4 GDC: Solving Equations
            • 1.2 Sequences & Series
              • 1.2.1 Language of Sequences & Series
                • 1.2.2 Arithmetic Sequences & Series
                  • 1.2.3 Geometric Sequences & Series
                    • 1.2.4 Applications of Sequences & Series
                    • 1.3 Financial Applications
                      • 1.3.1 Compound Interest & Depreciation
                        • 1.3.2 Amortisation & Annuities
                      • 2. Functions
                        • 2.1 Linear Functions & Graphs
                          • 2.1.1 Equations of a Straight Line
                          • 2.2 Further Functions & Graphs
                            • 2.2.1 Functions
                              • 2.2.2 Graphing Functions
                                • 2.2.3 Properties of Graphs
                                • 2.3 Modelling with Functions
                                  • 2.3.1 Linear & Piecewise Models
                                    • 2.3.2 Quadratic & Cubic Models
                                      • 2.3.3 Exponential Models
                                        • 2.3.4 Direct & Inverse Variation
                                          • 2.3.5 Sinusoidal Models
                                            • 2.3.6 Strategy for Modelling Functions
                                          • 3. Geometry & Trigonometry
                                            • 3.1 Geometry Toolkit
                                              • 3.1.1 Coordinate Geometry
                                                • 3.1.2 Arcs & Sectors
                                                • 3.2 Geometry of 3D Shapes
                                                  • 3.2.1 3D Coordinate Geometry
                                                    • 3.2.2 Volume & Surface Area
                                                    • 3.3 Trigonometry
                                                      • 3.3.1 Pythagoras & Right-Angled Triganometry
                                                        • 3.3.2 Non Right-Angled Trigonometry
                                                          • 3.3.3 Applications of Trigonometry & Pythagoras
                                                          • 3.4 Voronoi Diagrams
                                                            • 3.4.1 Voronoi Diagrams
                                                              • 3.4.2 Toxic Waste Dump Problem
                                                            • 4. Statistics & Probability
                                                              • 4.1 Statistics Toolkit
                                                                • 4.1.1 Sampling & Data Collection
                                                                  • 4.1.2 Statistical Measures
                                                                    • 4.1.3 Frequency Tables
                                                                      • 4.1.4 Linear Transformations of Data
                                                                        • 4.1.5 Outliers
                                                                          • 4.1.6 Univariate Data
                                                                            • 4.1.7 Interpreting Data
                                                                            • 4.2 Correlation & Regression
                                                                              • 4.2.1 Bivariate data
                                                                                • 4.2.2 Correlation Coefficients
                                                                                  • 4.2.3 Linear Regression
                                                                                  • 4.3 Probability
                                                                                    • 4.3.1 Probability & Types of Events
                                                                                      • 4.3.2 Conditional Probability
                                                                                        • 4.3.3 Sample Space Diagrams
                                                                                        • 4.4 Probability Distributions
                                                                                          • 4.4.1 Discrete Probability Distributions
                                                                                            • 4.4.2 Expected Values
                                                                                            • 4.5 Binomial Distribution
                                                                                              • 4.5.1 The Binomial Distribution
                                                                                                • 4.5.2 Calculating Binomial Probabilities
                                                                                                • 4.6 Normal Distribution
                                                                                                  • 4.6.1 The Normal Distribution
                                                                                                    • 4.6.2 Calculations with Normal Distribution
                                                                                                    • 4.7 Hypothesis Testing
                                                                                                      • 4.7.1 Hypothesis Testing
                                                                                                        • 4.7.2 Chi-squared Test for Independence
                                                                                                          • 4.7.3 Goodness of Fit Test
                                                                                                            • 4.7.4 The t-test
                                                                                                          • 5. Calculus
                                                                                                            • 5.1 Differentiation
                                                                                                              • 5.1.1 Introduction to Differentiation
                                                                                                                • 5.1.2 Applications of Differentiation
                                                                                                                  • 5.1.3 Modelling with Differentiation
                                                                                                                  • 5.2 Integration
                                                                                                                    • 5.2.1 Trapezoid Rule: Numerical Integration
                                                                                                                      • 5.2.2 Introduction to Integration
                                                                                                                        • 5.2.3 Applications of Integration


                                                                                                                        DOWNLOAD PDF

                                                                                                                      Author: Daniel

                                                                                                                      Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.


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