1. Sampling

2. Content Concepts Steps in the sampling process Types of samples
Random sampling
- Simple random sampling
- Systematic random sampling
- Stratified random sampling
- Cluster sampling
Geographic sampling
- Quadrat sampling
- Traverse sampling
- Point sampling

3. Concepts
At the core of inferential statistics is the distinction b/w a population and sample
Ideally, sample is a good representation of the population
Sampling error: uncertainty that arises from working with a sample rather than the entire population
Sampling bias: occurs when the procedures used to select the sample tend to favor the inclusion of individuals in the population with certain population characteristics.

4. Sampling frame: or population frame, an ordered list of the individuals in a population.
Example: telephone survey for evaluation of voter preferences for political parties. Population frame : all eligible voters
Target population: the set of all individuals relevant to a particular study
Sampled population: all the individuals in the sampling frame. – voters with telephones – bias

5. Sampling design: a procedure used to select individuals from the sampling frame for the sample.
Two classes of sampling design: probability samples and nonprobability samples
Probability sample: the probability of any individual member of the population being picked for the sample can be determined.

6. Non-probability samples
Convenience/accessibility sample: only convenient/accessible members of the population are selected.
Quota sample: obtain a representative sample by instructing interviewers to acquire data from given subgroups of the population.
Volunteer sample: consists of individuals who self-select from the population.

7. Random sampling design
1. Simple random sample: each possible sample of a given size n has equal probability of being selected.
2. Systematic random sampling: every nth element of the sampling frame is chosen, beginning with a randomly chosen point.
- is likely as good as a simple random sample, provided that the arrangement of the individuals in the sampling frame is random.

8. 3. Stratified sample: obtained by forming classes, or strata, in the population and then selecting a simple random sample from each.

9. Cluster sampling: The population is first divided into mutually exclusive classes (usually defined on the basis of convenience). Next, certain clusters are selected by some random procedure for detailed study, however, a random sample is not drawn from within the clusters.
- Often give very poor results.
- Stratified samples are best and cluster samples are worst. Random samples lie
somewhere b/w these two.

10. Geographic Sampling
Traverse samples Quadrat samples Point samples

14. Point samples