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What else can I help you with?
How do you solve simple random sampling?
Put up a no trespassing sign. Could you be (a lot) more specific?
What is the problem of random sampling?
With random sampling, you are hoping to get a representative sample of a whole, however statistically you could get a sample that is very different from the whole it was selected from. The larger the sample proportion of the whole, the better your sample will be. For example, a sample of 10 out of 100 is not as good as 20 out of 100. The bigger the sample the closer to the actual whole average you will get.
What is the adwantages of simple randem sampling with example?
Simple random sampling offers several advantages, including ease of implementation and the ability to produce unbiased results, as every member of the population has an equal chance of being selected. This method minimizes sampling bias and simplifies statistical analysis. For example, if a researcher wants to study student preferences in a university with 1,000 students, they could randomly select 100 students using a random number generator, ensuring each student has an equal chance of being included in the sample.
A sampling error could occur from?
a poorly designed hypothesis
Is cluster sampling is a type of stratified random sampling?
No. Cluster sampling and stratifed random sampling are different, though often confused. (They may, however, be used in conjunction in some sampling designs.) Both are types of random sampling.STRATIFIED sampling involves identifying a variable that will break up your population into separate homogeneous groups (homogeneous in terms of the variable you are interested in). For example, suppose you want to know about the attitudes of kids about their future. Perhaps you have reason to believe this will change with time. If you collected a sample from high schools, you could stratify by grade, giving you 4 relatively homogeneous groups: freshmen, sophomores, juniors, seniors. Then, a common approach is to sample a similar number from each group.Sometimes, though, separating the groups isn't so clear cut. Perhaps you want to stratify based on religion. You can't tell this from looking at a person. So perhaps you collect sample data and apply strata after the fact! This can be useful, but there are some statistical techniques that require equal (or nearly) sample sizes for the strata.CLUSTER sampling involves breaking your population into fairly similarly sized groups called clusters (try googling MSE for an example). But now you want each cluster to contain a heterogeneous mix of individuals. Then, you take a random selection of these clusters and completely enumerate inside of those selected clusters. The problem with cluster sampling is that the cluster has now become your sample unit, instead of individuals which is what you probably hoped. This can be used for counting species, or just for contacting certain populations like apartment dwellers, nursing home residents, etc. The clusters could be apartment buildings in a city. So instead of taking a random sample of apartment dwellers, you would actually randomly select a few of the buildings and talk to everyone inside! Often, this is much more cost efficient. :)
Example of stratified random sampling?
In stratified sampling, the population to be sampled is divided into groups (strata), and then a simple random sample from each strata is selected. For example, a state could be separated into counties, a school could be separated into grades. These would be the 'strata'.
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with a criminal justice e?
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with an information technology example.
How do you solve simple random sampling?
Put up a no trespassing sign. Could you be (a lot) more specific?
What is the problem of random sampling?
With random sampling, you are hoping to get a representative sample of a whole, however statistically you could get a sample that is very different from the whole it was selected from. The larger the sample proportion of the whole, the better your sample will be. For example, a sample of 10 out of 100 is not as good as 20 out of 100. The bigger the sample the closer to the actual whole average you will get.
What problems or limitations could prevent a truly random sampling and how can they be prevented?
Cost: A random sample of schools across a country would require travel all over the country. This is expensive in time and money. The solution is to use cluster sampling.Non-compliance: Subject sharing a common characteristic cannot or will not provide the information sought. Not much that can be done unless you can invoke statutory powers. However, even that may not work. A classic example of this was approx 1 million people who were "missed out" in the 1991 UK population census because they did not wish to be on the tax register, despite the fact that not returning the census form is a crime carrying a heavy fine (GBP 1,000).Unknown (or incomplete) sampling frame: You cannot assign a sampling probability if the total number in the population is not known. Use capture-recapture first to estimate population size.
When is sampling neccessary?
Sampling is necessary in a few places. It could be when eating, painting and building.
What is explanatory research?
Explanatory research is research conducted in order to explain any behaviour in the market. It could be done through using questionnaires, group discussions, interviews, random sampling, etc.
What is the adwantages of simple randem sampling with example?
Simple random sampling offers several advantages, including ease of implementation and the ability to produce unbiased results, as every member of the population has an equal chance of being selected. This method minimizes sampling bias and simplifies statistical analysis. For example, if a researcher wants to study student preferences in a university with 1,000 students, they could randomly select 100 students using a random number generator, ensuring each student has an equal chance of being included in the sample.
Could the value of the sampling error be zero?
Yes, it could.
What are some problems that could prevent people from land reclamation?
It is very expensive and it is unnatural
What problems could prevent your car from starting up?
Some problems which could prevent your car from starting is, maybe you didn't put your keys in all the way. Also, you might need an oil change, or some more gas in your tank.
A sampling error could occur from?
a poorly designed hypothesis
