An Introduction to Inferential Statistics

“Inferences about a population from a random sample drawn from it is called inferential statistics.”

Inference is the reasoning involved in drawing a conclusion or making a logical judgment on the basis of evidence and prior conclusions rather than direct observation.”

Population:

The entire collection of items that is the focus of concern is our population.

Sample:

A small part of the population which is selected to investigate the properties of the population is known as a sample.

Sampling:

The part of statistical practice concerned with the selection of individual observations intended to concede some knowledge about a population of concern specially for the purpose of statistical inference.

Or simply the process of selecting a sample out of population is called Sampling.

Advantages of Sampling:

1. Time saving

2. Reduce cost

3. Greater Scope

4. Destructive Tests

5. May provide more reliable information

6. Reliability of estimates

Types of Sampling

• Biased Sampling
• Un-biased Sampling
• Probability Sampling
• Non-probability Sampling

1. Probability Sampling

If each unit in the population has known but not necessarily equal probability of selection in the sample, then this is known as probability or random sampling.

Types of Probability Sampling

• Simple Random Sampling
• Stratified Sampling
• Systematic Random Sampling
• Cluster Sampling or Multi-stage Sampling
• Multi-phase sampling

2. Non-probability Sampling

If personal judgment is used to decide that which sampling units are to be included in the sample, then this is known as non-probability or non-random sampling.

Types of non-probability sampling

• Purposive Sampling
• Quota Sampling