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Yes because it gives a representation of all the data! <3
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What is the difference between a simple random sample and a stratified random sample?
Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.
What are the examples of sampling?
1. Simple Random Sampling (SRS) - For SRS, every element has an equal probability of being chosen. In fact, any pair, triplet, and so on of elements have an equal chance of random selection. Sometimes, SRS can have problems because the randomness of the sample does not represent the population. For example, a SRS of one hundred people will likely produce about fifty men and fifty women, but it's also possible that there will only be ten men and ninety women selected due to natural sampling variation. 2. Systematic Sampling - For this type of sampling, every nth element is sampled. For example, if names were to be sampled through systematic sampling, every tenth name would be picked from the telephone book. However, this type of sampling may result in an unrepresentative sample of the population. 3. Stratified Sampling - When a population has certain categories, samples can be purposely collected from each strata (category). For example, there may be different strata for age groups if the person sampling is interested in variations between differences in age. One problem with stratified sampling is that it requires a more expensive cost than simple random sampling or systematic sampling. 4. Convenience Sampling - This type of sampling involves drawing the easiest samples to reach from the population. This may include surveying customers outside of a grocery store. Because the sample is limited to a certain time/day, it is unrepresentative of the entire population.
Are probability and random sampling anonymous?
Probability is a branch of mathematics and so is not linked with any individual and so is anonymous. Random sampling may or may not include information that will allow the contributor to be identified. So it may or may not be anonymous.
What are the advantages and disadvantages of random sampling?
The main advantage is that the sample is representative of the population and the mean of the sample is an unbiased estimate of the population mean. Also, characteristics of other statistics based on the sample are well understood. However, sometimes it may not be possible to gather valid information from a sampling unit and then the sample is no longer random. This can be either because the sampling unit cannot be located or has been compromised by external factors. This can be particularly serious if the "missing" units share a common characteristic. Also, simple random samples may not include any units representing characteristics that are rare in the population - but important in the context of the experiment.
What is the advantages of random sampling?
Please read related link on what defines a simple random sample. When a sample is done randomly, then every item in the population has an equal chance of being selected. An advantage of random sampling is unbiased statistics. An unbiased statistic has the characteristic that as the sample size increases, the statistics from the sample approaches the true values of the population. This is true if the probability distribution of the population is not changing with time, or as a result of being sampled. Using a random sampling method does not guarantee statistics free of bias. For instance, if I wanted to produce a biased result, I might ask loaded questions. I might also pick particular city, say Chicago, and ask people at random for their favorite team. Obviously, my statistic is not valid outside of Chicago. A second advantage is that the statistical analysis related to sample distributions, hypothesis testing, and sample size determinations assume that the sample is a simple random sample. Remember. the goal of all sampling methods is to obtain information that is representative of the population that is under study. It may not be practical to do a random sample in many cases. For example, suppose I want to know how many people die before age 45 in the world. My random sample would have to include people any country. You can find more information on random sampling and other methods by searching under random sampling methods.
Different types of random and non random sampling?
stratified sampling, in which the population is divided into classes, and random samples are taken from each class;cluster sampling, in which a unit of the sample is a group such as a household; andsystematic sampling, which refers to samples chosen by any system other than random selection.
Is questionnaire probability sampling technique?
A questionnaire has little to do with sampling technique. Sampling technique is to do with who gets the questionnaire and that can be any sampling technique: the questionnaire can be sent to everyone (census), to a random sample, stratified random samples, to random samples in clusters, by quota or convenience. Or a pile of questionnaires can be left for respondents to pick up - self-selection.
Advantages of stratified sampling?
There are many advantages of stratified sampling. These include:Your sample better represents the population and so it is easier to generalise.There is little likelihood of a freak sampleAs you take people from all the different segments of the population, you can be sure that any results will not be due to something specific to one segment.
Are there any Advantages of quota random sampling?
Yes, if under simple random sampling there are likely to be too few representatives from a certain subset of the population in which you might have an interest.
Is there any Advantages of simple random sampling?
Cheap, simple, easily applied to a small population ensures bias is not introduced
What is the difference between a simple random sample and a stratified random sample?
Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.
What are the examples of sampling?
1. Simple Random Sampling (SRS) - For SRS, every element has an equal probability of being chosen. In fact, any pair, triplet, and so on of elements have an equal chance of random selection. Sometimes, SRS can have problems because the randomness of the sample does not represent the population. For example, a SRS of one hundred people will likely produce about fifty men and fifty women, but it's also possible that there will only be ten men and ninety women selected due to natural sampling variation. 2. Systematic Sampling - For this type of sampling, every nth element is sampled. For example, if names were to be sampled through systematic sampling, every tenth name would be picked from the telephone book. However, this type of sampling may result in an unrepresentative sample of the population. 3. Stratified Sampling - When a population has certain categories, samples can be purposely collected from each strata (category). For example, there may be different strata for age groups if the person sampling is interested in variations between differences in age. One problem with stratified sampling is that it requires a more expensive cost than simple random sampling or systematic sampling. 4. Convenience Sampling - This type of sampling involves drawing the easiest samples to reach from the population. This may include surveying customers outside of a grocery store. Because the sample is limited to a certain time/day, it is unrepresentative of the entire population.
What is random sampleng?
Random sampling can be defined as the selection of a random sample; each element of the population had an equal chance of been selected. Random sampling is used in psychology, statistics, math, sociology, movement and research.
Are probability and random sampling anonymous?
Probability is a branch of mathematics and so is not linked with any individual and so is anonymous. Random sampling may or may not include information that will allow the contributor to be identified. So it may or may not be anonymous.
Example of multistage sampling?
the combinitoin of any random samples is called multistage samplinag. it is the expensive form of cluster samling. when each elements in cluster are expensive then we use multistage sampling.
What are the advantages and disadvantages of random sampling?
The main advantage is that the sample is representative of the population and the mean of the sample is an unbiased estimate of the population mean. Also, characteristics of other statistics based on the sample are well understood. However, sometimes it may not be possible to gather valid information from a sampling unit and then the sample is no longer random. This can be either because the sampling unit cannot be located or has been compromised by external factors. This can be particularly serious if the "missing" units share a common characteristic. Also, simple random samples may not include any units representing characteristics that are rare in the population - but important in the context of the experiment.
Why should your sampling be random?
Random sampling prevents any emergent patterns inherent in a non-random selection process. Second, random samples of the population provide the widest representation of the population as a whole (given that the sample is large enough). This is why there is emphasis and the quantity of the study group-- the larger the population being analyzed the more accurate a representation it will be during the course of the study.
