Sampling Techniques
⭐ Higher Tier Content
Sampling is the process of selecting a smaller group from a population to collect data. Different sampling techniques are used to improve fairness, representativeness and reliability of conclusions.
Why Sampling Is Used
It is often impractical to collect data from an entire population.
Sampling allows:
• quicker data collection
• reduced cost and effort
• conclusions to be drawn about a larger group
The method chosen affects how reliable and unbiased the results are.
Random Sampling
In random sampling, every member of the population has an equal chance of being selected.
This reduces bias and increases fairness.
Random sampling may be carried out by:
• numbering each member of the population
• using random number tables or generators
Because selection is purely by chance, personal choice does not influence the sample.
Random sampling works best when a full list of the population is available.
A random sample is not guaranteed to be perfectly representative, but it is fair
Systematic Sampling
In systematic sampling, individuals are selected at regular intervals from a list.
For example:
• every 5th person
• every 10th item
The method usually involves:
• choosing a random starting point
• selecting every nth member after that
Systematic sampling is quick and easy to apply.
However, it can be biased if the list contains a repeating pattern that matches the interval.
Stratified Sampling
In stratified sampling, the population is divided into groups called strata.
Each group represents a characteristic such as:
• age group
• gender
• year group
A sample is then taken from each stratum, often in proportion to its size.
This ensures that important groups are represented fairly.
Stratified sampling is especially useful when the population is uneven or contains distinct subgroups.
This method often gives the most representative sample
Comparing Sampling Techniques
Random sampling is fair but may miss smaller groups.
Systematic sampling is simple but can introduce bias if patterns exist.
Stratified sampling is more complex but improves representativeness.
The best method depends on:
• the population
• the data needed
• practical constraints
Choosing the right technique improves the quality of conclusions.
Common Errors to Avoid
Common mistakes include:
• choosing participants based on convenience
• using systematic sampling without a random start
• failing to include all strata in stratified sampling
• confusing sampling methods
Always explain how the sample was chosen.
Key Points to Remember
Sampling selects a smaller group from a population.
Random sampling gives everyone an equal chance.
Systematic sampling uses regular intervals.
Stratified sampling ensures key groups are represented.
The sampling method affects fairness and reliability.
Understanding and using different sampling techniques allows data to be collected in a fair and meaningful way, leading to more reliable statistical conclusions.