Sampling Techniques
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Key Questions

Saving time and money! And give to inferential statistics a reason to exist ^_^
I'll give you an example: I live in italy. Suppose that a certain company wants to make a study on how italians approach the world of work after completing school: the population will be all the italians (more or less).
Italy has 61 million inhabitants.
So this company would have to make 61 million forms or 61 million calls (ecc..) to collect all the data required. Hope you get my point, that would be impossible!
So it's common sense to extract a sample from the population (it has to be a RANDOM sample!) and then make the study on it.
Once the informations of interest have been extract from the data of the sample, inferential statistics can provide a "final judgement" of what could be the behaviour of the population, with great gain in time and money (and a little loss in accuracy, but it's fair enough ;) ).

Answer:
Sampling is a technique of selecting a representative part of a population for the purpose of determining characteristics of the population
Explanation:
Sampling is necessary because we usually cannot gather data from the entire population due to large or inaccessible population or lack of resources. Even in relatively small populations, the data may be needed urgently and including everyone in the population in data collection may take too long.
Sampling is the only method we got when the data gathering process damage the items from which we required data. 
Answer:
In fact sampling techniques are of two categories viz (i)Random Sampling Techniques and (ii) Nonrandom sampling techniques. Then there are the mixed sampling techniques.
Explanation:
Simple random sampling
Stratified Random Sampling
Systematic Random Sampling
Cluster Sampling
Quota Sampling and
Multistage sampling. the first one is the simplest form of Random Sampling
The Rest are Mixed Sampling devices. If further explanation is required ask individual questions.