Sampling Theory
What is Sampling Theory?
The entire group of individuals or objects about which information is desired.
Key formula / rule: Sampling Error (Conceptual)
Key points
- Define population, sample, census, parameter, and statistic.
- Explain the need for sampling.
- Differentiate between probability and non-probability sampling methods.
- Identify and describe common types of probability sampling (SRS, Stratified, Systematic, Cluster).
Common exam trap
Confusing population with sample.
Definitions
- Term
Population
- Meaning
The entire group of individuals or objects about which information is desired.
- Term
Sample
- Meaning
A subset of the population selected for study.
- Term
Census
- Meaning
A study that collects data from every member of a population.
- Term
Parameter
- Meaning
A numerical characteristic of a population (e.g., population mean, population variance).
- Term
Statistic
- Meaning
A numerical characteristic of a sample (e.g., sample mean, sample variance), used to estimate a population parameter.
- Term
Sampling Error
- Meaning
The difference between a sample statistic and the true population parameter, arising from the fact that a sample is not a perfect representation of the population.
- Term
Non-Sampling Error
- Meaning
Errors that occur during data collection, processing, analysis, or interpretation, not related to the sampling process itself (e.g., measurement error, non-response bias).
Learning objectives
Define population, sample, census, parameter, and statistic.
Explain the need for sampling.
Differentiate between probability and non-probability sampling methods.
Identify and describe common types of probability sampling (SRS, Stratified, Systematic, Cluster).
Identify and describe common types of non-probability sampling (Convenience, Quota, Purposive).
Understand the concepts of sampling error and non-sampling error.
Formulae
- Name
Sampling Error (Conceptual)
- Note
This is a conceptual definition; actual calculation depends on the parameter and statistic being estimated.
- Expression
Sampling Error = Population Parameter - Sample Statistic
Prerequisites
Basic understanding of statistical terms like population, data, mean, median, mode.
Familiarity with the concept of data collection and analysis.
Common mistakes
Confusing population with sample.
Not understanding the difference between probability and non-probability sampling methods.
Assuming a small sample is always unrepresentative.
Ignoring potential biases introduced by non-random sampling.
Confusing sampling error with non-sampling error.
Keywords
Sampling
Population
Sample
Census
Probability Sampling
Non-Probability Sampling
Simple Random Sampling
Stratified Sampling
Systematic Sampling
Cluster Sampling
Convenience Sampling
Quota Sampling
Purposive Sampling
Sampling Error
Non-Sampling Error
Parameter
Statistic
Practice preview
A researcher selects students from a university by choosing the first 50 students who walk into the library. Which type of sampling is this?…
medium
Consider a population of 1000 individuals. If a simple random sample of size 100 is drawn, and then replaced before the next draw, what is this sampling method called?…
hard
Which sampling technique involves selecting every k-th element from a list after a random start?…
easy