Sampling is concerned with the selection of a subset of individuals from within a population to estimate characteristics of the whole population. Researchers rarely survey the entire population because the cost of a census is too high. The three main advantages of sampling are that the cost is lower, data collection is faster, and since the data set is smaller it is possible to ensure homogeneity and to im-prove the accuracy and quality of the data.

**Concept of Population**

Sampling is concerned with the selection of a subset of individuals from within a population to estimate characteristics of the whole population. Researchers rarely survey the entire population because the cost of a census is too high. The three main advantages of sampling are that the cost is lower, data collection is faster, and since the data set is smaller it is possible to ensure homogeneity and to improve the accuracy and quality of the data.

**Techniques of Sampling**

There are two broader techniques of sampling: Probability Sampling or Random Sampling and Non-probability sampling, among which only Random Sampling can be used for statistical investigation.

**Probability Sampling or Random Sampling**

Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. Examples of random sampling include:

**Simple Random Sampling**

1. **Without Replacement:** One deliberately avoids choosing any member of the population more than once.

2. **With Replacement: **One member can be chosen more than once.

**Systematic Sampling**

Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Suppose you are talking data from every 10th person entering into a mall.

**Stratified Sampling**

Where the population embraces a number of distinct categories or “strata‖, each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected.

Where the population embraces a number of distinct categories or “strata‖, each stra-tum is then sampled as an independent sub-population, out of which individual ele-ments can be randomly selected.

male, full-time: 90

male, part-time: 18

female, full-time: 9

female, part-time: 63

Total: 180

and we are asked to take a sample of 40 staff, stratified according to the above categories.

The first step is to find the total number of staff (180) and calculate the percentage in each group.

% male, full-time = 90 / 180 = 50%

% male, part-time = 18 / 180 = 10%

% female, full-time = 9 / 180 = 5%

% female, part-time = 63 / 180 = 35%

This tells us that of our sample of 40,

50% should be male, full-time.

10% should be male, part-time.

5% should be female, full-time.

35% should be female, part-time.

50% of 40 is 20.

10% of 40 is 4.

5% of 40 is 2.

35% of 40 is 14.

Another easy way without having to calculate the percentage is to multiply each group size by the sample size and divide by the total population size (size of entire staff):

male, full-time = 90 x (40 / 180) = 20

male, part-time = 18 x (40 / 180) = 4

female, full-time = 9 x (40 / 180) = 2

female, part-time = 63 x (40 / 180) = 14

**Non-Probability Sampling**

In non — probability sampling, we cannot assign any probability to the selected sample. Nonprobability sampling techniques cannot be used to infer from the sample to the general population.

Examples of nonprobability sampling include:

**Convenience, Haphazard or Accidental sampling — **members of the population are chosen based on their relative ease of access. To sample friends, co-workers, or shoppers at a single mall, are all examples of convenience sampling.

**Judgmental sampling or Purposive sampling — **The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being re-searched.

**Sampling Bias**