Revise Sampling for Cambridge 9609 (3.2.3)

Sampling is the process of selecting a representative subset of individuals from a larger group, known as the 'population', to conduct market research. It is often impractical, too expensive, or time-consuming to survey every single person in the target market. By studying a carefully selected sample, a business can gain insights and make inferences about the entire population's characteristics, preferences, and behaviours. The fundamental goal is to gather data that is valid and reliable enough to inform business decisions, such as marketing strategies or new product development, without incurring the cost of a full census. The effectiveness of this process hinges on how well the sample mirrors the population it is intended to represent.

Probability (or random) sampling ensures that every member of the population has a known, non-zero chance of being selected. This method is considered statistically robust and is more likely to produce a representative sample, minimising sampling bias. Key techniques include: Simple Random Sampling, where each individual has an equal chance of being chosen (like a lottery); Systematic Sampling, where individuals are selected at regular intervals from a list (e.g., every 50th customer); and Stratified Sampling, where the population is divided into relevant subgroups (strata), and a random sample is drawn from each. While these methods are highly credible, they can be more complex, time-consuming, and expensive to implement than non-probability alternatives.

Non-probability sampling involves selecting a sample based on the subjective judgement of the researcher or convenience, rather than random selection. As a result, not all members of the population have a chance of being included. The two main methods are Quota Sampling, where researchers select a specific number of individuals from different subgroups (e.g., 50 males and 50 females), and Convenience Sampling, which involves using participants who are most easily accessible (e.g., interviewing shoppers at a local centre). While these methods are quick, easy, and inexpensive, they carry a high risk of bias and may not produce a sample that accurately represents the target population, limiting the generalisability of the findings.

The size of the sample is a critical consideration, but it is not the only factor determining accuracy. While a larger sample size generally reduces sampling error and increases the confidence level in the results, the principle of diminishing returns applies. More importantly, the sample must be representative, meaning it accurately reflects the key characteristics of the population. A large but biased sample is far less valuable than a smaller, representative one. Sampling bias occurs when the selection process systematically favours certain outcomes or individuals, leading to a skewed and misleading representation of the population. This can invalidate the entire research effort and lead to poor strategic decisions based on flawed data.

Random — names from database, lottery selection; best for statistical validity.

Stratified — ensure age/income/region proportions match population; good for segmented markets.

Quota — quick street interviews filling quotas; risk of interviewer bias choosing approachable people.