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Lessons for this topic haven’t been written yet. The objectives below show exactly what the syllabus expects, so you can plan with them in the meantime.
What Cambridge says you should be able to do, in our words. Codes are the topic number plus the objective’s position.
Understand the distinction between a sample and a population, and appreciate the need for randomness in choosing samples.
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Explain in simple terms why a given sampling method may be unsatisfactory.
Includes an elementary understanding of using random numbers to produce random samples. Knowledge of particular sampling methods, such as quota or stratified sampling, is not required.
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Recognise that a sample mean can be regarded as a random variable, and use the facts that E(X̄) = μ and Var(X̄) = σ²/n.
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Use the fact that X̄ has a normal distribution if X has a normal distribution.
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Use the Central Limit Theorem where appropriate.
Only an informal understanding of the Central Limit Theorem (CLT) is required: for large sample sizes, the distribution of a sample mean is approximately normal.
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Calculate unbiased estimates of the population mean and variance from a sample, using either raw or summarised data.
Only a simple understanding of the term 'unbiased' is required, e.g. that although individual estimates vary, the process gives an accurate result 'on average'.
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Determine and interpret a confidence interval for a population mean in cases where the population is normally distributed with known variance or where a large sample is used.
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Determine, from a large sample, an approximate confidence interval for a population proportion.
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