Sampling Techniques Flashcards
What is the purpose of inferential statistics?
They allow us to make predictions or inferences about a population based on a sample.
What are the two main applications of inferential statistics?
Estimation and hypothesis testing.
What is a population in statistics?
The entire group we want to generalize about.
Why do we use samples instead of studying entire populations?
Because collecting data from an entire population is costly, time-consuming, and unnecessary.
What is a sample?
A carefully chosen subset of the population used to make inferences.
What is a statistic?
A mathematical characteristic of a sample, used to estimate parameters.
What is a parameter?
A mathematical characteristic of a population.
What is a representative sample?
A sample that closely matches the characteristics of the population.
What is bias in sampling?
When a sample does not accurately represent the population due to selection errors.
What are two sources of bias in sampling?
Convenience sampling and personal leanings.
What is the goal of probability sampling?
To ensure every member of the population has an equal chance of being selected (EPSEM).
What are four types of probability sampling techniques?
Simple Random Sampling (SRS)
Systematic Sampling
Stratified Sampling
Cluster Sampling
What is simple random sampling?
A method where every case in the population has an equal chance of being selected.
What is required for simple random sampling?
A complete list of the population (sampling frame) and a random selection process.
How does systematic sampling work?
The first case is randomly selected, and then every k-th case is chosen.
What is stratified sampling?
A method where the population is divided into subgroups (strata), and a random sample is taken from each.
Why use stratified sampling?
To ensure adequate representation of smaller subgroups within the population.
If we want to compare employment rates across different majors, what sampling method should we use?
Stratified sampling to ensure smaller majors are properly represented.
What is cluster sampling?
A method where the population is divided into clusters, and some clusters are randomly selected for full data collection.
Why use cluster sampling?
It is cost-effective and useful when a full population list is unavailable.
f we want to study sociology students across Canada, what sampling method should we use?
Cluster sampling by selecting a random set of universities and surveying all sociology students there.
What are two types of non-probability sampling?
Convenience sampling and snowball sampling.
What is convenience sampling?
Selecting participants who are readily available, such as students in a class.
What is snowball sampling?
Using existing participants to recruit new participants, often for hard-to-reach populations.
Can non-probability sampling be used to generalize findings?
No, because it does not ensure a representative sample.
What is a sampling distribution?
A theoretical distribution of sample results for all possible samples of a given size.
What three distributions are involved in inferential statistics?
Sample distribution - based on collected data
Population distribution - unknown characteristics
Sampling distribution - theoretical, based on probability laws
What are three key properties of the sampling distribution?
It is normally shaped.
Its mean equals the population mean.
Its standard deviation (standard error) is
What does the First Theorem state about sampling distributions?
If samples are drawn from a normal population, the sampling distribution will be normal with:
What does the Central Limit Theorem (CLT) state?
If we take large enough samples from any population, the sampling distribution will approach normality.
What is the significance of CLT?
It allows us to use inferential statistics even when the original population is not normally distributed.
What is a recommended sample size for CLT to apply?
Generally,
n≥100
