In our daily lives, we apply
sampling in many dimensions. For instance, before you buy a commodity with many
varieties, you normally choose the best. For sure we can’t run away from sampling as it
plays a vital rolein decision-making process. In statistics, sampling involves
examining an entire material under study. It enables the statisticians to make
meaningful inferences about a particular population under investigation. The assumptions made are that sample and
population means are equal. Sampling distribution is the statics distribution
from a large number of samples in a specific population. It applies a wide
distribution of frequencies with different outcomes of statics population.
Standard sampling methodologies applied in statistics includes;
·
The random sampling. Here every individual in observation has an
equal probability of being put in a sample.
Its 0.5% significance applied while calculating the sample facilitates a
researcher in finding the relationship that exists in sample and
population.
·
Probability sampling. Here every entity in the
population has a higher than zero probability of being a sample.
·
Non- probability sampling. In this methodology
of sampling some elements of the population has no probability to get into the
sample.
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