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Sampling Fundamentals

the process of obtaining information about an entire population by examining only a part of it

is

Need for sampling

1. save time and money.

2. the only way when population contains infinitely many members.

3. usually enables to estimate the sampling errors

Fundamental definitions

Universe/ popuation: ‘Universe' is the total of the items or units in any field of inquiry, ‘population’ is the total of items about which information is desired

Sampling frame: list containing all such sampling

units

Sampling design: definite plan for obtaining a sample from the sampling

frame.

Statisitc(s) and parameter(s): A statistic is a characteristic of a sample,a parameter is

a characteristic of a population

Sampling error: amount of inaccuracy in the information collected

Precision: range within which the population average (or other parameter) will lie in accordance with the reliability

Confidence level and significance level: confidence level or reliability is the expected percentage of times that the actual value will fall within the stated precision limits and the significance level indicates the likelihood that the answer will fall outside that range.

Sampling distribution: sampling distribution can be taken as the mean of the universe. The most important are (1) sampling distribution of mean; (2) sampling distribution of proportion; (3) student’s ‘t’ distribution; (4) F distribution; and

(5) Chi-square distribution.

Other fundamentals

Central Limit Theorem

Sampling Theory

Sandler's A-test

Concept of Standard Error

Estimation

it permits us to use sample statistics to make inferences about population parameters without knowing anything about the shape of the frequency distribution of that population

Study of relationships existing between a population and samples drawn from the population. Sampling theory is applicable only to random samples

The standard deviation of sampling distribution of a statistic

Alternate approach based on a simplification of t-test created by Joseph Sandler

In most statistical research studies, population parameters are usually unknown and have to be estimated from a sample.