1. A population is all units--people or things--possessing the attributes or characteristics that interest the researcher.
2. A sample, or subset, is selected from the population through probability or nonprobability sampling.
3. Generalizability is the extent to which conclusions drawn from a sample can be extended to a population.
4. Sampling error is the degree to which a sample differs from population characteristics.
5. Probability sampling ensures that the selected sample is sufficiently representative of the population as every person or element has an equal chance of being selected.
6. In a simple random sample, every person or element has an equal chance of being selected for a study.
7. In systematic sampling, every nth, for example every 14th, element is selected for the sample.
8. A stratified random sample first groups members according to categories of interest before random techniques are used.
9. Cluster sampling is used when all members of elements of a population cannot be identified and occurs in two stages: (1) the population is identified by its groups, and (2) then random sampling occurs within groups.
10. Nonprobability sampling weakens the representativeness of a sample to the population because it does not rely on random sampling; however, it is used when no other sampling technique will result in an adequate and appropriate sample.
11. Types of nonprobability sampling include convenience, volunteer, inclusion and exclusion, snowball, networking, purposive, and quota samples.
12. Sample size is estimated from the size of the population and the level of error a researcher is willing to tolerate.
13. Significance levels are set for each statistical test used in a research project; generally, the probability level of .05 is accepted as the standard in communication research.
14. Hypothesis testing is based on probability sampling techniques and the stated level of significance.
15. By convention, researchers are interested in the alternative hypothesis but statistically test the null hypothesis.
16. Hypothesis testing is an act of decision making-accepting the alternative hypothesis or retaining the null hypothesis.
17. Type I and Type II errors occur when researchers accept or reject results as valid when the opposite is true.
