# 1.2

## 12 terms

### Simple Random Sample

Of N measurements from a population is a subset of the population selected in a manner such that every sample of size N form the population has an equal chance of being selected.

### Simulation

A numerical facsimile or representation of a real world phenomenon.

### Random Sampling

Use a simple random sample from the entire population.

### Stratified Sampling

Divide the entire population into distinct subgroups called strata. The strata are based on specific characteristics such as age, income education level, and so on. All member of the stratum share the specific characteristic. Draw random from each stratum.

### Systematic Sampling

Number all members of the population sequentially. Then, from a starting point selected at random, include every Kth member of the population in the sample.

### Cluster Sampling

Divide the entire population into pre-existing segments or clusters. The clusters are often geographic. Make a random selection of the clusters. Include every member of each selected cluster in the sample.

### Multistage Sampling

Use a variety of sampling methods to create successfully smaller groups at each stage. The final sample consists of clusters.

### Convience Sampling

Create a sample by using a data from population members that are readily available.

### Sampling Frame

A list of individuals from which a sample is actually selected.

### Undercoverage

Results from omitting population members from the sample frame.

### Sampling Error

The 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.

### Nonsampling Error

The result of poor sampling design, sloppy data collection, faulty measuring instruments, bias in questionnaires and so on.