Sampling and Sampling Distribution Flashcards
Characteristics of interest measurable or observable on each individual comprising the universe.
Variable
Set of all possible values of the variable.
Population
It is the selection of a part of the universe of interest to represent the whole by obtaining estimates of the parameters that can be validly generalized to the universe.
Sampling
It is the descriptive measure of the population.
Parameter
It is the selected part (a subset of the universe or population)
Sample
It is the descriptive measure of the sample.
Statistic
It is a list of elements of the population.
Sampling Frame
What are some of the different data sources that consist of the Sampling Frame?
- Population lists
- Students’ lists
- Credit cardholders’ lists
- Teachers’ lists
- Directories
- Maps.
It is a method in which elements of the population get an equal opportunity to be selected as a representative sample.
Probability Sampling
The selection process is random sampling.
Probability Sampling
Results are unbiased due to its conclusive nature.
Probability Sampling
The basis of inference is statistical.
Probability Sampling
A method of selecting n units out of the N units in the population so that every individual or item in the sampling frame has the same chance of selection as other individuals or items.
Simple Random Sampling
A sample is selected by taking at random n elements in the population, one from each of the N groups containing k elements.
Systematic Sampling
A sample is selected by taking independent random samples from each of the population’s mutually exclusive subpopulations or stratum.
Stratified Sampling
A sample is selected by taking all or a subset of randomly chosen subpopulations or clusters.
Cluster Sampling
It is usually a natural or preexisting designation such as cities, municipalities, villages, or schools.
Cluster
It can be viewed as an extension of cluster sampling.
Multi-Stage Sampling
It is a method in which the population elements are not pre-specified and have an unequal opportunity to be a sample.
Non-probability Sampling
The selection process involved is arbitrary.
Non-probability Sampling
Results are biased due to its exploratory nature.
Non-probability Sampling
The basis of inference is analytical.
Non-probability Sampling
It is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher.
Convenience Sampling
Individuals are chosen to be part of the sample with a specific purpose in mind.
Judgmental or Purposive Sampling
It begins by identifying a small number of individuals who meet the inclusion criteria in a study. The researcher then asks them to recommend others they know who also meet the selected criteria.
Snowball Sampling
It involves a non-random selection of individuals based on some pre-determined quota.
Quota Sampling
It represents the major characteristics of the population based on the proportionality of each category.
Proportional quota sampling
It does not require specific numbers that correspond to the population’s proportion.
Non-proportional quota sampling
It states that “as the sample size becomes bigger, the sampling distribution of the sample mean
can be approximated by a normal probability distribution”.
Central Limit Theorem
It is a probability distribution based on many samples of size 𝑛 from a given population.
Sampling Distribution of a Statistic
A statistic that is arrived out through repeated sampling from a larger population.
Sampling Distribution
It describes a range of possible outcomes of a statistic, such as the mean or mode of some variables, as it truly exists in a population.
Sampling Distribution
