0
Do you only need one sample to use a sampling distribution to make a decision?
Wiki User
∙ 14y agoWant this question answered?
Be notified when an answer is posted
Add your answer:
Earn +20 pts
Define sampling design?
Sampling is a method of selecting experimental units from a population so that we can make decision about the population. Sampling design is a design, or a working plan, that specifies the population frame,sample size, sample selection, and estimation method in detail. Objective of the sampling design is to know the characteristic of the population.
Why the random sample is important in design of experiment?
There are circumstances when it is important and others when it is not. If, for example, you wanted a sample of all schools in the country, it would make more sense to go for cluster sampling. A lot of market research work will require quota sampling. So the supremacy of a random sample is a myth.
How are sampling distributions generated using the empirical sampling approach?
Depending on the number of samples involved, you either simulate all possible samples or you simulate taking a large number of samples. The distribution of he sampling statistic can be calculated from these. Here's a Python script that demonstrates how to do this in a simple form. Suppose you want to experiment with the sampling distribution of the sample mean for samples of size 5 drawn from a normal population with mean 0 and standard deviation 1. There are, of course, mathematical results that establish the exact sample distribution of this statistic. Let's pretend we don't know that. Usually it's necessary to generate many, many samples to establish a sampling distribution. For the purposes of this exercise this code generates only 20. from scipy.stats import norm N = norm ( 0., 1. ) ## create source of N(0,1) deviates x_bars = [ ] ## create place to hold sample x_bar values for s in range ( 20 ) : ## run experiment 20 times x_bars . append ( sum ( N . rvs ( 5 ) ) / 5. ) ## make & store sample x_bar values x_bars . sort ( ) ## form the so-called order statistics for the sample for i, x_bar in enumerate ( x_bars ) : ## print empirical distribution function print '%.2f %.2f' % ( ( i + 1 ) / 20., x_bar, ) When I ran this code I got the following: 0.05 -1.65 0.10 -0.28 0.15 -0.25 0.20 -0.10 0.25 -0.09 0.30 -0.08 0.35 0.01 0.40 0.05 0.45 0.10 0.50 0.12 0.55 0.13 0.60 0.19 0.65 0.41 0.70 0.46 0.75 0.50 0.80 0.55 0.85 0.67 0.90 0.79 0.95 0.94 1.00 1.05 If you plot these using the column on the left as the x-axis you will get the so-called empirical distribution function. The sample values can also be used in a variety of ways to obtain estimates of the sampling probability density. Please see the link for the basics.
What about accidental sampling?
Accidental sampling is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a sample population selected because it is readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct such a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing.
Under what cicumstances should these sampling methods recommended cluster sampling systematic sampling multistage sampling non-probability sampling give examples in each case?
For decades the go to place to find the best quality reviews on pretty much any product in the US has been consumerreports.org. Consumer reports has been around for forever, and they review products that come onto the market and give pretty solid objective reviews. They can help make your decision easier.
Define sampling design?
Sampling is a method of selecting experimental units from a population so that we can make decision about the population. Sampling design is a design, or a working plan, that specifies the population frame,sample size, sample selection, and estimation method in detail. Objective of the sampling design is to know the characteristic of the population.
Does use of chi-square demand a random sample?
Well, sort of. The Chi-square distribution is the sampling distribution of the variance. It is derived based on a random sample. A perfect random sample is where any value in the sample has any relationship to any other value. I would say that if the Chi-square distribution is used, then every effort should be made to make the sample as random as possible. I would also say that if the Chi-square distribution is used and the sample is clearly not a random sample, then improper conclusions may be reached.
What difference between Statistical Sampling and non-statistical sampling?
Statistical sampling is an objective approach using probability to make an inference about the population. The method will determine the sample size and the selection criteria of the sample. The reliability or confidence level of this type of sampling relates to the number of times per 100 the sample will represent the larger population. Non-statistical sampling relies on judgment to determine the sampling method,the sample size,and the selection items in the sample.
Sampling is a method of estimating population?
Sampling makes it possible to make assumptions about the larger population based on a small sample. This is beneficial in the study of population and demographics.
Why the random sample is important in design of experiment?
There are circumstances when it is important and others when it is not. If, for example, you wanted a sample of all schools in the country, it would make more sense to go for cluster sampling. A lot of market research work will require quota sampling. So the supremacy of a random sample is a myth.
How are sampling distributions generated using the empirical sampling approach?
Depending on the number of samples involved, you either simulate all possible samples or you simulate taking a large number of samples. The distribution of he sampling statistic can be calculated from these. Here's a Python script that demonstrates how to do this in a simple form. Suppose you want to experiment with the sampling distribution of the sample mean for samples of size 5 drawn from a normal population with mean 0 and standard deviation 1. There are, of course, mathematical results that establish the exact sample distribution of this statistic. Let's pretend we don't know that. Usually it's necessary to generate many, many samples to establish a sampling distribution. For the purposes of this exercise this code generates only 20. from scipy.stats import norm N = norm ( 0., 1. ) ## create source of N(0,1) deviates x_bars = [ ] ## create place to hold sample x_bar values for s in range ( 20 ) : ## run experiment 20 times x_bars . append ( sum ( N . rvs ( 5 ) ) / 5. ) ## make & store sample x_bar values x_bars . sort ( ) ## form the so-called order statistics for the sample for i, x_bar in enumerate ( x_bars ) : ## print empirical distribution function print '%.2f %.2f' % ( ( i + 1 ) / 20., x_bar, ) When I ran this code I got the following: 0.05 -1.65 0.10 -0.28 0.15 -0.25 0.20 -0.10 0.25 -0.09 0.30 -0.08 0.35 0.01 0.40 0.05 0.45 0.10 0.50 0.12 0.55 0.13 0.60 0.19 0.65 0.41 0.70 0.46 0.75 0.50 0.80 0.55 0.85 0.67 0.90 0.79 0.95 0.94 1.00 1.05 If you plot these using the column on the left as the x-axis you will get the so-called empirical distribution function. The sample values can also be used in a variety of ways to obtain estimates of the sampling probability density. Please see the link for the basics.
What about accidental sampling?
Accidental sampling is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a sample population selected because it is readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct such a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing.
You are in charge of promoting a new flavor of toothpaste yet to be produced in a toothpaste manufacturing unit which sampling techniques will you use to get the required data from a population?
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business, sampling is widely used for gathering information about a populationIt is incumbent on the researcher to clearly define the target population. There are no strict rules to follow, and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives of the study.Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn.Sampling methods are classified as either probability or non-probability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In non-probability sampling, members are selected from the population in some non-random manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non probability sampling, the degree to which the sample differs from the population remains unknown.· Random samplingis the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.· Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.· Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.· Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.· Judgment sampling is a common non-probability method. The researcher selects the sample based on judgment. This is usually an extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.· Quota samplingis the non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.· Snowball sampling is a special non-probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.If I were an officer to promote a new flavour of toothpaste yet to be produced, I would use RANDOM SAMPLING METHODØ Random sampling:Random sampling- all members of the population have an equal chance of being selected as part of the sample. You might think this means just standing in the street and asking passers-by to answer your questions. However, there would be many members of the population who would not be in the street at the time you are there; therefore, they do not stand any chance of being part of your sample. To pick a random sample, it is necessary to take all the names on the electoral register (A list of all the people who live in a particular area) and pick out, for example, every fiftieth name. This particular person needs to be interviewed to make the sample truly random. Random sampling is very expensive and time consuming, but gives a true sample of the population.Types of Random sample:A simple random sample is selected so that all samples of the same size have an equal chance of being selected from the population.A self-weighting sample, also known as an EPSEM (Equal Probability of Selection Method) sample, is one in which every individual, or object, in the population of interest has an equal opportunity of being selected for the sample. Simple random samples are self-weighting.Stratified sampling involves selecting independent samples from a number of subpopulations, group or strata within the population. Great gains in efficiency are sometimes possible from judicious stratification.Cluster sampling involves selecting the sample units in groups. For example, a sample of telephone calls may be collected by first taking a collection of telephone lines and collecting all the calls on the sampled lines. The analysis of cluster samples must take into account the intra-cluster correlation which reflects the fact that units in the same cluster are likely to be more similar than two units picked at randomØ Pros and Cons:1. There are lot of bias in Random sampling2. It is feasible and simple as the sampling is done on a random basis.3. Can make sample units in groups.4. Very expensive and time consuming, but gives a true result of the population5. While in toothpaste case, the users can given a sample piece of toothpaste randomly to get the feedback or their opinion from the chosen populationConclusion: Though the Random sampling has couple of de-merits it will help to figure out the result from the chosen population. While all other also may provide the result which may not be best comparing to the sampling method which I have chosen (Random Sampling)
What are the advantages of purposive sampling?
Purposive sampling can allow the researcher to make generalizations about results. Purposive sampling also provides the researcher with many different sampling techniques.
Under what cicumstances should these sampling methods recommended cluster sampling systematic sampling multistage sampling non-probability sampling give examples in each case?
For decades the go to place to find the best quality reviews on pretty much any product in the US has been consumerreports.org. Consumer reports has been around for forever, and they review products that come onto the market and give pretty solid objective reviews. They can help make your decision easier.
Why are you using sampling?
Usually we are interested in the characteristics of large populations of items or people. It would often prove costly or impossible to measure these characteristics for the entire population. We therefore measure them for a carefully selected sample of the population and attempt to make scientific inferences about the entire population from the characteristics of the sample.
What are the uses of sampling?
(1) A sample may save money (as compared with the cost of a complete census) when absolute precision is not necessary. (2) A sample saves time, when data are desired more quickly than would be possible with a complete census. (3) A sample may make it possible to concentrate attention on individual cases. (4) In industrial uses, some tests are destructive (for example, testing the length of time an electric bulb will last) and can only be performed on a sample of items. (5) Some populations can be considered as infinite, and can, therefore, only be sampled. A simple example is an agricultural experiment for testing fertilizers. In one sense, a census can be considered as a sample at one instant of time of an underlying causal system which has random features in it. (6) Where non-sampling errors are necessarily large, a sample may give better results than a complete census because non-sampling errors are easier to control in smaller-scale operations
