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What is the chief aim of probability sampling is to be able to select?
Wiki User
∙ 7y ago
The chief aim is to find a representative sample; that is, a sample which reflects the properties of the population, as a whole.
Wiki User
∙ 6y ago
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Advantages and dis advantages of sampling?
There are many advantages and disadvantages of sampling. These advantages include being able to try what you need before you buy.
What is sampling and why is it necessary?
Sampling is taking a section of the entire group you are studying and only studying them. Its important to be able to make studies at levels that are practible, but still have enough info to make them correct.
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 distinguishes a probability sample from a non probability sample?
I believe you meant to ask: What distinguishes a random sample from a non random sample? A random sample means the selection or sampling from the population is by chance. Looking at the data, one might not be able to tell if the sample is random or selective. Consider a marketing survey which is included everytime you buy an item online. Random or non-random? It is a survey of recent customers, and probably a pretty good one. But it is not a random selection of all customers who have made purchases with clients.
What are the conditions under which you can expect to be able to use a binomial distribution to model a probability distribution?
Two independent outcomes with constant probabilities.
What is targeted sampling in research studies?
I would like to sample the signal Xa(t) =1+cos(10 *pi*t) using sampling frequency fs=8 Hz. How can I calculate this? ANSWER: Your signal has a frequency component of 5hz (from the equation: 2*pi*f*t = 10*pi*t, therefore f=5). The Nyquist rate for this signal (the minimum sampling rate required to reconstruct the signal) is then 10Hz, and even at that rate the amplitude of the sampled signal will be reduced unless you can somehow synchronize the sampling with the peaks/troughs of the cosine signal. If you sample at 8Hz you will not be able to reconstruct the signal at all.
Advantages and dis advantages of sampling?
There are many advantages and disadvantages of sampling. These advantages include being able to try what you need before you buy.
What is the shape of this sampling distribution?
No, your telepathic powers are not sending me the correct image to be able to answer the question!
What is sampling and why is it necessary?
Sampling is taking a section of the entire group you are studying and only studying them. Its important to be able to make studies at levels that are practible, but still have enough info to make them correct.
Why were statistics developed?
To be able to understand the probability of chance for an occurrence and to further understand probability
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.
How do you select all columns and rows that have data in them within excel?
Press the F5 key. Then press click Special on the dialog box that opens. You will then be able to select types of data and formulas to find and be able to select them.
What sampling rate from a sound card would you require if you want to be able to record CD sound quality?
The standard CD is two-channel 16-bit PCM encoding at a 44.1 kHz sampling rate per channel.
What is an example of a select able mutation?
Durg resistance
What is probability of an impossible event?
If the event is truly impossible, it has a zero probability of occurring. The definition of impossible is "not able to occur, exist, or be done," so that means there's no chance of it happening.
What distinguishes a probability sample from a non probability sample?
I believe you meant to ask: What distinguishes a random sample from a non random sample? A random sample means the selection or sampling from the population is by chance. Looking at the data, one might not be able to tell if the sample is random or selective. Consider a marketing survey which is included everytime you buy an item online. Random or non-random? It is a survey of recent customers, and probably a pretty good one. But it is not a random selection of all customers who have made purchases with clients.
Crew ranks on a sailing ship?
There are many ranks on a sailing ship. Some of these include: the captain master, chief officer, able seaman, chief engineer, and chief cook.
