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DP IB Maths: AI SL

Revision Notes

Home / IB / Maths: AI SL / DP / Revision Notes / 4. Statistics & Probability / 4.7 Hypothesis Testing / 4.7.4 The t-test


4.7.4 The t-test


Two-Sample Tests

What is a t-test?

  • A t-test is used to compare the means of two normally distributed populations
  • In the exam the population variance will always be unknown

What assumptions are needed for the t-test?

  • The underlying distribution for each variable must be normal
  • In the exam you will need to assume the variance for the two groups are equal
    • You will need to use the pooled two-sample t-test

What are the steps for a pooled two-sample t-test?

  • STEP 1: Write the hypotheses
    • H0 : μx = μy
      • Where μx and μy are the population means
      • Make sure you make it clear which mean corresponds to each population
      • In words this means the two population means are equal
    • H1 : μx < μy or H1 : μx > μy or H1 : μx ≠ μy
      • The alternative hypothesis will depend on what is being tested (see sections for one-tailed and two-tailed tests)
  • STEP 2: Enter the data into your GDC
    • Enter two lists of data – one for each sample
    • Choose the pooled option
    • Your GDC will then give you the p-value
  • STEP 3: Decide whether there is evidence to reject the null hypothesis
    • Compare the p-value with the given significance level
      • If p-value < significance level then reject H0
      • If p-value > significance level then accept H0
  • STEP 4: Write your conclusion
    • If you reject H0
      • There is sufficient evidence to suggest that the population mean of X is bigger than/smaller than/different to the population mean of Y
      • This will depend on the alternative hypothesis
    • If you accept H0
      • There is insufficient evidence to suggest that the population mean of X is bigger than/small than/different to the population mean of Y
      • Therefore this suggests that the population means are equal

One-tailed Tests

How do I perform a one-tailed t-test?

  • A one-tailed test is used to test one of the two following cases:
    • The population mean of X is bigger than the population mean of Y
      • The alternative hypothesis will be: H1 : μx > μy
      • Look out for words such as increase, bigger, higher, etc
    • The population mean of X is smaller than the population mean of Y
      • The alternative hypothesis will be: H1 : μx < μy
      • Look out for words such as decrease, smaller, lower, etc
  • If you reject the null hypothesis then
    • This suggests that the population mean of X is bigger than the population mean of Y
      • If the alternative hypothesis is H1 : μx > μy
    • This suggests that the population mean of X is smaller than the population mean of Y
      • If the alternative hypothesis is H1 : μx < μy

Worked Example

The times (in minutes) for children and adults to complete a puzzle are recorded below.

Children

3.1

2.7

3.5

3.1

2.9

3.2

3.0

2.9

 

Adults

3.1

3.6

3.5

3.6

2.9

3.6

3.4

3.6

3.7

3.0

The creator of the puzzle claims children are generally faster at solving the puzzle than adults. A t-test is to be performed at a 1% significance level.

a)
Write down the null and alternative hypotheses.

4-7-4-ib-ai-sl-t-test-one-tail-a-we-solution

b)
Find the p-value for this test.

4-7-4-ib-ai-sl-t-test-one-tail-b-we-solution

c)
State whether the creator’s claim is supported by the test. Give a reason for your answer.

4-7-4-ib-ai-sl-t-test-one-tail-c-we-solution

Two-tailed Tests

How do I perform a two-tailed t-test?

  • A two-tailed test is used to test the following case:
    • The population mean of X is different to the population mean of Y
      • The alternative hypothesis will be: H1 : μx ≠ μy
      • Look out for words such as change, different, not the same, etc
  • If you reject the null hypothesis then
    • This suggests that the population mean of X is different to the population mean of Y
    • You can not state which one is bigger as you were not testing for that
      • All you can conclude is that there is evidence that the means are not equal
      • To test whether a specific one is bigger you would need to use a one-tailed test

Worked Example

In a school all students must study either French or Spanish as well as maths. 18 students in a maths class complete a test and their scores are recorded along with which language they study.

Studies French

61

82

77

80

99

69

75

71

81

Studies Spanish

74

79

83

66

95

79

82

81

85

The maths teacher wants to investigate whether the scores are different between the students studying each language. A t-test is to be performed at a 10% significance level.

a)
Write down the null and alternative hypotheses.

4-7-4-ib-ai-sl-t-test-two-tail-a-we-solution

b)
Find the p-value for this test.

4-7-4-ib-ai-sl-t-test-two-tail-b-we-solution

c)
Write down the conclusion to the test. Give a reason for your answer.

4-7-4-ib-ai-sl-t-test-two-tail-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
                                                                                                                      Daniel Finlay

                                                                                                                      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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