what is Simple Random Sample

what is Simple Random Sample
what is Simple Random Sample

what is Simple Random Sample
中文版：
简单随机抽样法
Simple random sampling
又称,单纯随机抽样.作为一种抽样方法,就是在总体单位中不进行任何分组、排队等,完全排除任何主观的有目的的选择,采用纯粹偶然的方法从母体中选取样本.
这种方法更能体现出总体中每个子体的机会完全相等,选出的样本与总体特性接近,是各种几率抽样中比较简便易行的一种方法.
为实现抽样的随机化,可采用抽签、查随机数值表等办法.这个办法的优点就抽样误差小,缺点是抽样手续比较繁杂.在实际工作中,真正做到总体中的每个个体被抽到的机会完全一样是不容易的.
简单随机抽样的特点：
①它要求被抽取样本的总体的个数是有限,这样,便于通过随机抽取的样本对总体进行分析.
②它是从总体中逐个地进行抽取.这样,便于在抽样实践中进行操作.
③它是一种不放回抽样.由于抽样实践中多采取不放回抽样,使其具有较广泛的实用性,而且由于所抽签的样本中没有被重复抽取的个体,便于进行有关的分析和计算.
④它每一次抽取时总体中的各个个体有相同的可能性被抽到,从而保证了这种抽样方法的公平性.
In statistics,a simple random sample is a subset of individuals (a sample) chosen from a larger set (a population).Each individual is chosen randomly and entirely by chance,such that each individual has the same probability of being chosen at any stage during the sampling process,and each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals (Yates,Daniel S.; David S.Moore,Daren S.Starnes (2008).The Practice of Statistics,3rd Ed..Freeman.ISBN 978-0-7167-7309-2.).This process and technique is known as simple random sampling,and should not be confused with Random Sampling.
In small populations and often in large ones,such sampling is typically done "without replacement" ('SRSWOR'),i.e.,one deliberately avoids choosing any member of the population more than once.Although simple random sampling can be conducted with replacement instead,this is less common and would normally be described more fully as simple random sampling with replacement ('SRSWR').Sampling done without replacement is no longer independent,but still satisfies exchangeability,hence many results still hold.Further,for a small sample from a large population,sampling without replacement is approximately the same as sampling with replacement,since the odds of choosing the same sample twice is low.
An unbiased random selection of individuals is important so that in the long run,the sample represents the population.However,this does not guarantee that a particular sample is a perfect representation of the population.Simple random sampling merely allows one to draw externally valid conclusions about the entire population based on the sample.
Conceptually,simple random sampling is the simplest of the probability sampling techniques.It requires a complete sampling frame,which may not be available or feasible to construct for large populations.Even if a complete frame is available,more efficient approaches may be possible if other useful information is available about the units in the population.
Advantages are that it is free of classification error,and it requires minimum advance knowledge of the population other than the frame.Its simplicity also makes it relatively easy to interpret data collected via SRS.For these reasons,simple random sampling best suits situations where not much information is available about the population and data collection can be efficiently conducted on randomly distributed items,or where the cost of sampling is small enough to make efficiency less important than simplicity.If these conditions are not true,stratified sampling or cluster sampling may be a better choice.
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