0
Add your answer:
Earn +20 pts
Why might researchers study just a sample of a large population rather than the whole population?
Because the whole population might be too large to sample. A good example is the population of the world. At nearly 7 billion people, it would be unrealistic to sample each person to determine some factor that you are looking at. Generally, we sample a subset of the population, taking into account differences (or errors) that might result, in this case, regional and cultural, in order to estimate the behavior of the larger population.
Is it possible for sample not normal to be from normal population?
Yes. You could have a biased sample. Its distribution would not necessarily match the distribution of the parent population.
What You decide you want your sample to be representative of the population. Which sampling process would provide the most representative sample?
A simple random sample.
When taking a systematic random sample of size n every group of size n from the population has the same chance of being selected.?
No, that would be a random sample.
In statistics what would you call the conclusions drawn about a population?
INFERENCES Any calculated number from a sample from the population is called a 'statistic', such as the mean or the variance.
What would a sampling error of zero mean?
The sampling error is the error one gets from observing a sample instead of the whole population. The bigger it is, the less faith you should have that your sample represents the true value in the population. If it is zero, your sample is VERY representative of the population and you can trust that your result is true of the population.
Why might researchers study just a sample of a large population rather than the whole population?
Because the whole population might be too large to sample. A good example is the population of the world. At nearly 7 billion people, it would be unrealistic to sample each person to determine some factor that you are looking at. Generally, we sample a subset of the population, taking into account differences (or errors) that might result, in this case, regional and cultural, in order to estimate the behavior of the larger population.
How large would your sample have to be for appropriate estimation of the whole population?
First you have chose an estimator for what you want to know about the population. In general the level of variability in the result that any estimator provides will depend on the variability in the population. Therefore, the greater the variability in the population the larger your sample size must be. You will also need to decide how much precision is required in your estimate. The more precision you require the greater your sample size will have to be.
What is bias sample?
Sampling bias occurs when the sampling frame does not reflect the characteristics of the population which is being tested. Biased samples can result from problems with either the sampling technique or the data-collection method. Essentially, the group does not reflect the population which is supposed to be represented in the given survey or test. For example: If the question being asked in a survey was "do American's prefer Coca-Cola or Pepsi?" and all people asked were under 18 and from California, there would be a sampling bias as the sampling frame would not accurately represent "American's".
Why is it so important to have a representative sample?
If the sample is not representative of the population, then the characteristics of the sample are not the characteristics of the population. Example: If I want to estimate the percentage of the population that are men, and my sample is the school's football team, my estimate would be that 100% of the population is comprised of men. What went wrong with my survey ? Simple. The football team is not a representative sample of the population, at least not as regards gender.
Is it possible for sample not normal to be from normal population?
Yes. You could have a biased sample. Its distribution would not necessarily match the distribution of the parent population.
How can one avoid sampling error?
To avoid sampling error, you should ensure that your sample is representative of the population, use random sampling techniques, increase the sample size when possible, and use stratified sampling if your population can be divided into subgroups. Additionally, verify the reliability of your data collection methods to minimize errors.
What is the best estimator of population mean?
The best estimator of the population mean is the sample mean. It is unbiased and efficient, making it a reliable estimator when looking to estimate the population mean from a sample.
What You decide you want your sample to be representative of the population. Which sampling process would provide the most representative sample?
A simple random sample.
What is the sample size definition?
Sample size is the number of samples arawn from a population. If you drew 20 samples, your sample size would be 20.
Why is sample preferred to population?
Sample is preferred to population because observing the population can be impossible due to its size. You take a random sample of the population and, with statistics, you can infer things about that population to various degrees of confidence based on the sample size and on other knowledge about the population. For instance, if you wanted to know how many people on earth have brown hair, you would not check all 6 billion people - you would create a sample set, say a few hundred, thousand, or whatever - count the number with brown hair - and then run your calculations.
What would it mean If the sampling error was zero?
The sample consisted of the entire population.
