Question

Types of probability:

Simple Random Sampling
A sampling procedure in which each member of the population has an equal probability of being included in the sample.

Systematic Sampling
It is when you choose every "nth" individual to be a part of the sample.

Stratified Random Sampling
It involves a method where a larger population can be divided into smaller groups that usually do not overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately.

Cluster Random Sampling
It is a way to select participants when they are geographically spread out randomly.

Non-probability sampling:

purposive sampling
A type of nonprobability sample that involves choosing sample members based on characteristics, experience or knowledge that would be useful for the research study

Quota sampling
A type of nonprobability sampling in which members of the study population are separated into groups based on characteristics and sample members are chosen from within each group.

Snowball sample
A type of nonprobability sampling in which researchers asked each sample member to suggest or refer other possible sample members can be very useful when members of the population are difficult to locate

Convenience sample
a form of a nonprobability sample using respondents who are convenient or readily accessible to the researcher—for example, employees, friends, or relatives

Answer 1

Sampling methods in research include simple random, stratified, systematic, and cluster sampling for a probabilistic approach, whereas convenience sampling is a non-probability approach that may result in bias as it relies on easily accessible subjects.

Step-by-step explanation:

In research, it is often impractical to study the entire population, so various sampling methods are utilized to represent the population adequately. One such method is simple random sampling, where each member of a population has an equal chance of being selected. This technique ensures that every subset of the population has the same probability of being chosen for the sample.

Stratified random sampling divides the population into strata or groups, ensuring that subgroups are adequately represented. Simple random sampling is then used to select a proportionate number of participants from each stratum. In systematic sampling, a list of population members is created, and every nth person is selected based on a random starting point.

With cluster sampling, the population is divided into clusters, such as geographical areas, and then whole clusters are randomly chosen, with all members of those clusters being included in the sample. Non-probability sampling methods like convenience sampling involve choosing participants that are easily accessible, which may lead to biased results since the sample may not be representative of the entire population.