Sampling is an important component of any piece of research because of the significant impact that it can have on the quality of your results/findings.

It is often impractical and sometimes undesirable to try and study the entire population.

A sample consists of only those units from our population of interest that we actually study (e.g., 500 or 1000 of these Facebook users).

In practice, the sample size that is selected for a study can have a significant impact on the quality of your results/findings, with sample sizes that are either too small or excessively large both potentially leading to incorrect findings. As a result, sample size calculations are sometimes performed to determine how large your sample size needs to be to avoid such problems.

Sampling bias occurs when the units that are selected from the population for inclusion in your sample are not characteristic of (i.e., do not reflect) the population. This can lead to your sample being unrepresentative of the population you are interested in.

We typically study a sample of that population rather than attempt to study the whole population . The purpose of sampling techniques is to help you select units (e.g., Facebook users) to be included in your sample (e.g., of 500 Facebook users).

Sampling is important in research to be able to produce accurate results. It is impractical and undesirable to study the whole population. If the sample is too small or excessively large, it may lead to incorrect findings. Sampling techniques may be used to find representative samples to avoid bias.

Non probability sample mehtod does not involve random selection and sampling like probably sampling. So we can say that it is not necessary to respect the population. Unlike probability sampling, it does not depend on rationale of probability theory. With probabilistic technique we can have a