Sampling:
Sampling is a fundamental aspect of statistics, but unlike the other methods of data collection, sampling involves choosing a method of sampling which further influences the data that you will result with. There are two major categories in sampling: probability and nonprobability sampling.
Probability Sampling:
Under probability sampling, for a given population, each element of that population has a chance of being picked to part of the sample. In other words, no single element of the population has a zero chance of being picked
The odd/chances/probability of picking any element is known or can be calculated. This is possible if we know the total number in the entire population such that we are then able to determine that odds of picking any one element.
Probability sampling involves random picking of elements from a population, and that is the reason as to why no element has a zero chance of being picked to be part of a sample.
Methods of Probability Sampling:
There are a number of different methods of probability sampling including:
NonProbability Sampling:
Unlike probability sampling, under nonprobability sampling certain elements of the population might have a zero chance of being picked. This is because we can't accurately determine the chances/probability of picking a given element so we do not know whether the odds of picking that element are zero or greater than zero. Nonprobability sampling may not always be a consequence of the sampler's ignorance of the total number of elements in the population but may be a result of the sampler's bias in the way he chooses the sample by excluding some elements.
Methods of NonProbability Sampling:
There are a number of different methods of Nonprobability sampling which include:
1. What is sampling in statistics? 
2. How is sampling different from a census? 
3. What are the different types of sampling methods? 
4. How does sample size affect the accuracy of statistics? 
5. What are the advantages of using sampling in statistics? 

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