What is the fundamental difference between a target population and a sample?

The target population is the entire group of interest (e.g., all voters in a country), while the sample is the specific subset of that group that actually participates in the research. The sample is used to make inferences about the population.

How does Stratified Sampling differ from Simple Random Sampling?

Simple Random Sampling gives everyone an equal chance but might miss small subgroups by chance. Stratified Sampling ensures those subgroups are represented by dividing the population into 'strata' and sampling proportionally from each.

What is the difference between Probability and Non-Probability sampling?

Probability sampling uses random selection so every member has a known chance of being picked, reducing bias. Non-probability sampling relies on convenience or researcher judgment, making it faster but less representative.

What is 'Selection Bias' and why is it a problem?

Selection bias occurs when the method of choosing participants systematically excludes certain groups. This makes the sample unrepresentative, meaning the study's findings cannot be accurately generalized to the target population.

Why is 'Volunteer Bias' a concern in self-selected samples?

People who volunteer for studies often differ from those who don't; they may be more helpful, more interested in the topic, or have more free time. These shared traits make the sample unrepresentative of the general population.

What is the risk of 'Periodicity' in Systematic Sampling?

If the list being sampled has a hidden recurring pattern that coincides with the sampling interval (e.g., every 10th person on a list happens to be a manager), the sample will be biased and not truly random.

Define 'Sampling Frame'.

A sampling frame is the actual list or database from which the sample is drawn (e.g., a school register or a telephone directory). It should ideally include every member of the target population.

What is 'Opportunity Sampling'?

Also known as convenience sampling, it involves selecting participants who are readily available and willing at the time of the study. It is the easiest method but often the most biased.

What does 'Generalizability' mean in a research context?

It is the extent to which the results of a study conducted on a sample can be applied to the broader target population. High generalizability requires a representative, unbiased sample.

Why is a larger sample size generally preferred?

Larger samples tend to reduce the impact of anomalies and sampling error, making the sample mean a more accurate reflection of the population mean. This increases the statistical power of the study.

Sampling Methods: Target Populations & Samples | AQA GCSE Psychology

1. Definition & Core Concepts

Target Population: This refers to the entire group of individuals that a researcher is interested in studying. It is defined by specific characteristics relevant to the research, such as 'all high school students in a specific city' or 'individuals over 65 with a specific medical condition'.
Sample: A sample is a smaller group of participants selected from the target population to take part in the study. Because it is often impossible or impractical to study an entire population, the sample serves as a manageable proxy.
Representativeness: A sample is considered representative if its characteristics (age, gender, socio-economic status, etc.) closely mirror those of the target population. This is the gold standard in sampling because it allows for high external validity.

Diagram showing a large circle representing the Target Population and a smaller circle representing the Sample, with an arrow indicating the sampling process.

2. Underlying Principles: Generalizability

Generalization: This is the extent to which findings from a sample can be applied to the target population as a whole. If a sample is biased (e.g., only including one gender when the population is mixed), the results cannot be generalized.
Sampling Error: This is the natural discrepancy that exists between a sample statistic and the corresponding population parameter. Even with random sampling, a sample may not perfectly represent the population due to chance.
Bias: Systematic error in the sampling process that results in an unrepresentative sample. For example, if a researcher only recruits participants from a local library, they may exclude people who do not read or who work during library hours.

3. Probability Sampling Methods

Simple Random Sampling: Every member of the target population has an equal mathematical chance of being selected. This is often achieved using a random number generator or drawing names from a container, ensuring no human bias in selection.
Systematic Sampling: Participants are selected using a fixed interval, known as the $k^{th}$ element. For example, if you need a sample of $n=100$ from a population of $N=1000$ , you would select every $10^{th}$ person ( $k = N/n$ ) from a complete list.
Stratified Random Sampling: The population is divided into mutually exclusive subgroups (strata) based on key characteristics (e.g., age groups). A random sample is then taken from each stratum in proportion to its size in the population to ensure all groups are represented.

4. Non-Probability Sampling Methods

Opportunity (Convenience) Sampling: This involves selecting participants who are easily available and willing to participate at the time of the study. While highly efficient and cost-effective, it often leads to biased samples that lack generalizability.
Volunteer (Self-Selected) Sampling: Participants actively choose to join the study, often in response to an advertisement. This can lead to bias because volunteers may share specific personality traits, such as being more motivated or having more free time than the general population.

5. Key Distinctions

Method	Selection Logic	Main Strength	Main Weakness
Random	Equal chance for all	Highly unbiased	Can still be unrepresentative by chance
Stratified	Proportional subgroups	Most representative	Very time-consuming and complex
Systematic	Every $k^{th}$ person	Objective and simple	Risk of 'periodicity' (hidden patterns)
Opportunity	Whoever is available	Fast and inexpensive	High risk of researcher bias

6. Exam Strategy & Tips

Identify the 'N' and 'n': When reading a scenario, always distinguish between the total group the researcher wants to talk about (Target Population) and the specific group they actually tested (Sample).
Check for Proportionality: If a question mentions 'matching the percentages of the population,' it is almost certainly referring to Stratified Sampling.
Evaluate Generalizability: If a sample is small or taken from a specific location (like one school), be cautious about claiming the results apply to everyone. Always look for potential sources of bias like 'only volunteers' or 'only people available at 10 AM'.

Sampling Methods: Target Populations & Samples

Summary

1. Definition & Core Concepts

2. Underlying Principles: Generalizability

3. Probability Sampling Methods

4. Non-Probability Sampling Methods

5. Key Distinctions

6. Exam Strategy & Tips

Sampling Methods: Target Populations & Samples

Summary

1. Definition & Core Concepts

2. Underlying Principles: Generalizability

3. Probability Sampling Methods

4. Non-Probability Sampling Methods

5. Key Distinctions

6. Exam Strategy & Tips