Question

What does probability (random) and non-probability (non-random) sampling mean? Give a short example of how each could be performed to collect data. What are the advantages and disadvantages between probability and non-probability sampling? "Random" is a word that is used too often throughout statistics. Find (or create) two examples of the word "random" being used to represent different meanings.

Answer 1

Random sampling:

Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. If for some reasons, the sample does not represent the population, the variation is called a sampling error.

NON RANDOM SAMPLING:

Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research.

Non-probability sampling is a sampling method in which not all members of the population have an equal chance of participating in the study, unlike probability sampling. Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample compared to pre-determined sample size). Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations.

  Examples of random sampling

Suppose the total workforce in organisations is 300 and to conduct a survey, a sample group of 30 employees is selected to do the survey. In this case, the population is the total number of employees in the company and the sample group of 30 employees is the sample. Each member of the workforce has an equal opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. But, there is always a possibility that the group or the sample does not represent the population as a whole, in that case, any random variation is termed as a sampling error.

Examples of non-random sampling

1. An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample.

2. In an organization, for studying the career goals of 500 employees, technically, the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata using quota sampling.

3. Researchers also use this type of sampling to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer to other subjects suffering from the same ailment to form a subjective sample to carry out the study.

Advantages and disadvantages of random sampling

Probability sampling gives you the best chance to create a sample that is truly representative of the population. Using probability sampling for finding sample sizes means that you can employ statistical techniques like confidence intervals and margins of error to validate your results.

major advantages:

• The absence of systematic error and sampling bias.
• Higher level of reliability of research findings.
• Increased accuracy of sampling error estimation.
• The possibility to make inferences about the population.

Disadvantage

Higher complexity compared to non-probability sampling. More time consuming. Usually more expensive than non-probability sampling.

Advantages and disadvantages of non-random sampling:

A major advantage with non-probability sampling is that — compared to probability sampling — it's very cost- and time-effective. It's also easy to use and can also be used when it's impossible to conduct probability sampling (e.g. when you have a very small population to work with).

  One major disadvantage of non-probability sampling is that it's impossible to know how well you are representing the population.

Different meaning of random:

1. One way it can be defined as the condition of unsystematic.

2. It can be defined for a person who is unidentified or unknown.

3. It can also be defined as the lack in uniformity in making a building.