Sampling and Sampling Distributions

W3 L2

Descriptive statistics

collecting, presenting, and describing data

Inferential Statistics

Drawing conclusions and/or making decisions concerning a population based only on sample data

Population

set of all items or individuals of interest

Sample

a subset of the population

why sample?

1. less time consuming

2. less costly

3. high precision

4. can save product

5. sometimes the only option

Random sampling

• Every unit of the population has the same probability of being included in the sample.

• Eliminates bias in the selection process

• aka probabiliy sampling

• a chance mechanism is used in the selection process

Non-random sampling

1. Every unit of the population does not have the same probability of being

   included in the sample.

2. aka non-probability sampling

3. open to selection bias

Random sampling techniques

1. Simple Random sample

2. Stratified Random sample(proporionate/disproportionate)

3. Systemic Random Sample

4. Cluster sampling

Non-random sampling methods

1. Convenience Sampling

2. Judgement Sampling

3. Quota Sampling

4. Snowball Sampling

Stratified random sample

• population is divided into non-overlapping subpopulations called strata

• a random sample is selected from each stratum

Systemic Sampling

• Convenient and relatively easy to administer

• population elements are an ordered sequence

• first sample element is randomly selected from the first k population elements.

• thereafter, sample elements are selected at a constant interval, k.

Cluster Sampling

• population is divided into non-overlapping clusters/areas

• a subset of the clusters is selected randomly for the sample

Convenience Sampling

Sample elements are selected for the convenience of the researcher

Judgement Sampling

Sample elements are selected by the judgment of the researcher

Quota Sampling

Sample elements are selected until the quota controls are satisfied

Snowball Sampling

Survey subjects are selected based on referral from other survey respondents

Types of errors

sampling and non-sampling errors

Types of sampling distributions

1. Samping distribution of sample mean

2. Samping distribution of sample proportion

3. Samping distribution of sample variance

Standard error of the mean

a measure of the variability in the mean from sample to sample

Central Limit theorem

the sampling distribution of the mean will always be normally distributed, as long as the sample size is large enough