1.2 vocab

1. 1.2 vocab

2. Simple Random Sample n measurements from a population is a subset of the population selected in a manner such that every sample of size n from the population has an equal chance of being selected

3. Simulation numerical facsimile or representation of a real-world phenomenon

4. Random Sampling use a simple random sample from the entire population

5. Stratified Sampling divide the entire population into distinct subgroups called strata. Draw random samples from each stratum

6. Systematic Sampling number all members of the population sequentially. Then, from a starting point selected at random, include very kth member of the population in the sample

7. Cluster Sampling divide the entire population into pre-existing segments or clusters. Make a random selection of clusters. Include every member of each selected cluster in the sample

8. Multistage Sampling use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters

9. Convenience Sampling create a sample by using data from population members that are readily available

10. Random sampling- use a simple random sample from the entire population • Stratified sampling- divide the entire population into distinct subgroups called strata. Draw random samples from each stratum • Systematic sampling- number all members of the population sequentially. Then, from a starting point selected at random, include very kth member of the population in the sample • Cluster sampling- divide the entire population into pre-existing segments or clusters. Make a random selection of clusters. Include every member of each selected cluster in the sample • Multistage sampling- use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters • Convenience sampling- create a sample by using data from population members that are readily available Sampling Techniques

11. Sampling Frame of individuals from which a sample is actually selected

12. Undercoverage when population members are omitted from the sample frame

13. Sampling Error difference between measurements from a sample and corresponding measurements from the respective population. It is caused by the fact that the sample does not perfectly represent the population

14. Nonsampling Error result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on