• The concept of a random sample, sources of bias in samples, and procedures to ensure randomness

• The concept of the sample proportion as a random variable

• The approximate normality of the distribution of proportions for large samples

• The concept of an interval estimates for a parameter associated with a random variable

• How to define the approximate margin of error for proportions.

Week 1: Sample means

• Understand the concept of random samples, sample distributions and sample means
• Calculate the mean and standard deviation of sample distributions and sample means
• Understand the behaviour of sample distributions and sample means for large sample sizes
• Understand, construct and interpret confidence intervals for sample means.

Week 2: Sample proportions

• Understand the concept of sample proportions and population proportions
• Calculate the mean and standard deviation of sample proportions and population proportions
• Understand the behaviour of sample proportions and population proportions for large sample sizes
• Understand, construct and interpret confidence intervals for population proportions.

Week 3: Significance tests

• Understand the concept of significance testing
• Estimate p-values and understand their role in hypothesis testing
• Understand and apply z-tests for population proportions
• Understand and apply t-tests for sample means.

Week 4: Assessment

There is a timed exam.