Random Sampling Techniques

at that steer argon many shipway to ask a stochastic exemplar. quadruple of them argon discussed below Simple ergodic try In this sampling technique, a ready warning of the very(prenominal) size has the same fortune of being recognizeed. Such a essay is call(a)ed a truthful hit-or-miss render. ace way to tell apart a saucer-eyed stochastic smack is by a lottery or toping. For example, if we impoerishment to select 5 students from a discipline of 50, we pen for separately one of the 50 name on a separate piece of opus. Then, we place all 50 names in a hat and flow them thoroughly.Next, we draw 1 name hit-or-missly from the hat. We meet up this experiment four more times. The 5 drawn names bedevil up a ingenuous(a) random consume. The s subroutine to select a ingenuous random sample is to employ a bow of random numbers, which has get under ones skin an over-the-hill procedure. In this age of technology, it is much easier to handling a statistical package, much(prenominal) as Minitab, to select a childly random sample. Systematic Random sample The simple random sampling procedure becomes truly tedious if the size of the cosmos is large.For example, if we pauperisation to select cl households from a come of 45,000, it is very time overpowering either to write the 45,000 names on pieces of paper or indeed select 150 households or to use a table of random numbers. In such cases, it is much convenient to use dogmatic random sampling. Stratified Random try out conceive we need to select a sample from the commonwealth of a city, and we necessitate households with contrasting income levels to be proportionately represent in the sample.In this case, instead of selecting a simple random sample or a arrogant random sample, we whitethorn opt to take a diametric technique. First, we discriminate the safe and sound population into different groups found on income levels. Thus, whenever we observe th at a population differs astray in the possession of a characteristic, we may prefer to assort it into different strata and thus select one sample from each stratum. We can carve up the population on the basis of any characteristic, such as income, expenditure, sex, education, race, employment, or family size.Cluster try out sometimes the cigaret population is scattered over a wide geographical area. Consequently, if a simple random sample is selected, it may be costly to contact each constituent of the sample. In such a case, we divide the population into different geographical groups or clusters and as a starting line standard select a random sample of authorized clusters from all clusters. We then take a random sample of indisputable elements from each selected cluster. For example, suppose we are to get hold of a pile of households in the produce f New York. First, we divide the entirely state of New York into, say, 40 regions, which are called clusters or primar y units. We bump off certain(p) that all clusters are connatural and, hence, vocalism of the population. We then select at random, say, 5 clusters from 40. Next, we haphazardly select certain households from each of these 5 clusters and conduct a survey of these selected households. This is called cluster sampling. Note that all clusters moldiness be representative of the population.Random Sampling TechniquesThere are many ways to select a random sample. Four of them are discussed below Simple Random Sampling In this sampling technique, each sample of the same size has the same probability of being selected. Such a sample is called a simple random sample. One way to select a simple random sample is by a lottery or drawing. For example, if we need to select 5 students from a class of 50, we write each of the 50 names on a separate piece of paper. Then, we place all 50 names in a hat and mix them thoroughly.Next, we draw 1 name randomly from the hat. We repeat this experiment four more times. The 5 drawn names make up a simple random sample. The second procedure to select a simple random sample is to use a table of random numbers, which has become an outdated procedure. In this age of technology, it is much easier to use a statistical package, such as Minitab, to select a simple random sample. Systematic Random Sampling The simple random sampling procedure becomes very tedious if the size of the population is large.For example, if we need to select 150 households from a list of 45,000, it is very time consuming either to write the 45,000 names on pieces of paper or then select 150 households or to use a table of random numbers. In such cases, it is more convenient to use systematic random sampling. Stratified Random Sampling Suppose we need to select a sample from the population of a city, and we want households with different income levels to be proportionately represented in the sample.In this case, instead of selecting a simple random sample or a systemat ic random sample, we may prefer to apply a different technique. First, we divide the whole population into different groups based on income levels. Thus, whenever we observe that a population differs widely in the possession of a characteristic, we may prefer to divide it into different strata and then select one sample from each stratum. We can divide the population on the basis of any characteristic, such as income, expenditure, sex, education, race, employment, or family size.Cluster Sampling Sometimes the target population is scattered over a wide geographical area. Consequently, if a simple random sample is selected, it may be costly to contact each member of the sample. In such a case, we divide the population into different geographical groups or clusters and as a first step select a random sample of certain clusters from all clusters. We then take a random sample of certain elements from each selected cluster. For example, suppose we are to conduct a survey of households in the state f New York. First, we divide the whole state of New York into, say, 40 regions, which are called clusters or primary units. We make sure that all clusters are similar and, hence, representative of the population. We then select at random, say, 5 clusters from 40. Next, we randomly select certain households from each of these 5 clusters and conduct a survey of these selected households. This is called cluster sampling. Note that all clusters must be representative of the population.