2 - Sampling Theory Flashcards
What is the difference between population and sample?
Population - the collection of units we want to generalize a set of findings or statistical model
Sample - a smaller (hopefully representative) collection of units from a population used to determine truths about that population
What is simple random sampling without replacement from a finite population?
A type of sampling that does not allow sampling units to occur more than once.
What is simple random sampling with replacement from a finite population?
A type of sampling in which sampling units are returned to the population after recording them, meaning that a sampling unit can occur more than once in a sample.
What is biased sampling without replacement from a finite population?
A type of sampling where every unit in the population does not have an equal chance of being in the sample.
What is random sampling?
A type of sampling where every member of the population has an equal chance of being selected.
What is stratified sampling?
A type of sampling where the target population is divided into important subcategories and members are selected in proportion to how often their category appears in the population
What is volunteer sampling?
A type of sampling where individuals in a population volunteer to be a part of the sample.
What is opportunity sampling?
A type of sampling where people that are available at the time needed are selected for the sample.
What is snowball sampling?
A type of sampling where currently enrolled participants help recruit future subjects for a study
Note: not all members of a population have an equal chance of being selected
What is a sample statistic?
The mean and standard deviation only describe the sample from which they were calculated.
What is a population parameter?
The mean and standard deviation are intended to describe the entire population.
What does the Law of Large Numbers say?
We get more accurate numbers from larger samples.
Ex. as the sample size increases, the sample mean approaches the true population mean
The Central Limit Theorem says that given a sufficiently sized sample, …
- The mean of the sampling distribution is the same as the population mean
- The standard deviation of the sampling distribution (standard error) gets smaller as the sample size increases
- The shape of the sampling distribution becomes normal as the sample size increases
How should we interpret a confidence interval?
If we replicate an experiment over and over again and computed a 95% confidence interval for each replication, then 95% of those intervals would contain the true mean.
