Identify types of data (qualitative and quantitative, discrete and continuous); understand populations and samples; use random and stratified sampling; and recognise sources of bias.

What this dot point is asking

The Eduqas statistics content begins with data and sampling: identifying the type of data (qualitative or quantitative, discrete or continuous), understanding the difference between a population and a sample, using random and stratified sampling, and recognising sources of bias. This is the foundation of the statistical handling cycle, and Eduqas tests it through both calculation (stratified sample sizes) and reasoning (why a sample is biased, how to improve it). It appears at both tiers, with the stratified-sample calculation and the bias-explanation being reliable questions.

Types of data

Data is classified first as qualitative or quantitative, and quantitative data is then discrete or continuous.

So shoe size is discrete, height is continuous, and team supported is qualitative. The distinction matters because continuous data is grouped into class intervals and shown with histograms, while discrete data can be shown with bar charts, so identifying the type points to the right chart.

Populations and samples

It is rarely practical to collect data from a whole population, so a sample stands in for it.

Sampling saves time and cost, but only works if the sample reflects the population. A census surveys the whole population, which is accurate but expensive, so sampling is the usual compromise.

Random and stratified sampling

Two named sampling methods appear in the specification.

Stratified sampling is the one most often calculated: find the sampling fraction $\dfrac{\text{sample size}}{\text{population size}}$, then multiply each group's size by it.

Recognising bias

Bias occurs when a sampling method systematically over- or under-represents part of the population.

A survey about reading habits conducted in a library will be biased because library users read more than the general population, so the result is unrepresentative. Common causes are sampling from the wrong place (gym-goers for exercise), self-selection (only motivated people respond), and leading questions. To improve a biased sample, choose a method that gives a fair cross-section of the whole population. Because Eduqas weights reasoning heavily, a strong answer explains precisely why the group is unrepresentative and what the effect on the results would be.