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So-called accidental sampling. Please see the link.
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∙ 10y ago
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What are the different non-probability sampling techniques?
The related web sites give a good idea of the types of non-random sampling. These include snowball, convenience, quota, self-selection, diversity, expert, and others. Non-randon sampling is usually done because it is less expensive, easier, and quicker than random sampling.
Advantages and disadvantages of random sampling?
Advantage -- Less effort, cost, work Disadvantage -- Less accuracy, information, difficulty of establishing true 'randomness" in some samplings.
What happens When a random sampling frame has a systematic pattern in the listing of sampling units rather than a random pattern?
You get a non-random sample and any analysis based on the assumption of randomly distributed variables is no longer valid. In particular, your estimates of any variables are likely to be biased and your error estimates (standard errors or sample variances) will be incorrect. Any inferences based on statistical tests will be less reliable and may be wrong.
What is the ideal sampling frequency?
Not less than double the highest frequency component of the signal you're sampling.
Why is a random sample better than a census?
A random sample is better than a census because it takes less time and costs less.
What are the reasons for choosing random sampling method for ones research work and what is random sampling method anyway?
because it is the simplest sampling technique which requires less time and cost.
Which sampling method is based on probability?
There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.
What are the different non-probability sampling techniques?
The related web sites give a good idea of the types of non-random sampling. These include snowball, convenience, quota, self-selection, diversity, expert, and others. Non-randon sampling is usually done because it is less expensive, easier, and quicker than random sampling.
Advantages and disadvantages of random sampling?
Advantage -- Less effort, cost, work Disadvantage -- Less accuracy, information, difficulty of establishing true 'randomness" in some samplings.
What happens When a random sampling frame has a systematic pattern in the listing of sampling units rather than a random pattern?
You get a non-random sample and any analysis based on the assumption of randomly distributed variables is no longer valid. In particular, your estimates of any variables are likely to be biased and your error estimates (standard errors or sample variances) will be incorrect. Any inferences based on statistical tests will be less reliable and may be wrong.
Why do you use sample populations?
e.g. you wanted to conduct a test on teenagers, if you wanted to test an entire population you would have to test every teenager in the world. BY using random sampling or stratisfied random sampling you can get fair results which represents the entire population and takes far less time.
What are the differences between sampling error and sampling bias?
Sampling bias is a known or unknown selection of data to be examined in an audit. There should be no bias if the sample is random. Ex ... look at the first item in the file folder. or examine all files for purchases over $10,000, or examine no files for sales less than $500. Sampling error, is the incorrect selection of files for an audit. Ex ... a random number generator tells you to audit file 1547, but you select 1457. Sampling error is also used to describe the fact that auditing a sample will NOT create the exact same answer as auditing every single file or transaction.
What is the ideal sampling frequency?
Not less than double the highest frequency component of the signal you're sampling.
Why is sampling preferred over census?
Less time and less cost for a sample
What is the difference between flat top sampling and natural sampling?
in flat top sampling the electronic circuit required for sampling are less complicated as compared to the one used in natural sampling, at demodulation of the sample it is very difficult to maintain the natural waveform of the natural sampling so flat top sampling can easily be demodulated.
How can you perform a sample selected in such a way that each member of the population has an equal probability of being included?
The short answer is "random sample," but that, unfortunately, is neither specific nor complete. It is not specific because there are forms of random sampling where selection probabilities are not constant. It is not complete because there are many different ways to conduct random sampling with equal selection probabilities. "Simple random sampling" occurs when you can perform a process that, for all practical purposes, behaves like writing down the identifier of each population member on a piece of paper, putting all the pieces into a box, mixing them thoroughly, and pulling out a few of them one by one (without replacing them in the box). Nowadays we use a computer to do this job, because it's faster and more reliable (it is notoriously difficult to mix pieces of paper perfectly randomly). The computer needs a complete list of all the population members: this is called a <i>sampling frame</i>. Here is an example of random sampling that is not simple but still selects every population member with equal probability. Suppose you want to sample half the students in a classroom of 30. Ask them to line up. Flip a fair coin: if it's heads, pick the first, third, ..., 29th in line. If tails, pick the second, fourth, ..., 30th. Any individual student has a 50% chance of being part of the sample, so each student has an equal probability of being included. However, if you lined up the students boy-girl-boy-girl, etc., the samples themselves wouldn't look very random: they will either be mostly boys or mostly girls. It's still random though, because it's determined by the flip of a coin. The example highlights a subtle but important property of a random sample: in many cases, you want the selection of population members to be <b>independent</b>. This means the probability of selecting one member is not affected by which other members are selected. In simple random sampling, independence holds; in the second example (a form of <i>gridded sampling</i>), there is complete dependence: no student can be chosen along with either of their neighbors in line, for instance. Simple random sampling is ideal for many purposes but often cannot be carried out in practice because it is not feasible (you might not be able to construct a sampling frame) or costs too much. Often, more complicated procedures, such as <i>hierarchical sampling</i>, are carried out to overcome these limitations. (An example of hierarchical sampling is when an epidemiologist selects a city at random, then selects households at random within the city, then selects children at random within each household to study. Doing it this way can require much less travel than selecting children at random from all over the state.) These procedures might or might not select population members with equal probability. Usually the selection is not independent, either. When the probabilities are unequal, they can be figured out and used as <i>weights</i> in statistical analysis of the data. Results can also be adjusted for lack of independence. A good, readable, non-technical introduction to sampling and simple random samples is the textbook <i>Statistics</i> by Freedman, Pisani, and Purves. Any edition is fine. Steven Thompson's book <i>Sampling</i> discusses dozens of different sampling procedures and explains the theory behind each one.
What will be the sampling frequency if FM equals 5V?
Not less than 10V
