What is the difference between a statistic and a parameter? If the Central Limit Theorem is...

What is the difference between a statistic and a parameter? If the Central Limit Theorem is so important what is the key benefit of having a sampling distribution that is normally distributed in research? Explain what is meant by ‘sampling error’. How is this important in statistical analysis?

Solutions

Expert Solution

Statistic is a function of the sample values that describes the sample and does not involve any unknown quantities.

Parameter is any constant that describes the population. When the parameter is specified the entire distribution of the data is known to us.

The sampling distribution bring normal is beneficial because the normal distribution has many good important properties. It is symmmetric unimodal distribution. Moreover it belongs to exponential family. Sample mean for normal distribution is unbiased, efficient and MVUE. It becomes easy to derive properties and reach our conclusions using normal distribution.

In order for our research we do not consider the entire population. We draw a random sample from the population. So the difference or deviations in the estimates because of considering only a part of the population is called as standard error. Many a times this sample may exhibit certain variability within it.. Such variabilities can be determined by sampling error. Moreover large sampling error indicates that the sample is not a proper representative of the population.

orchestra answered 1 year ago

Next > < Previous

Related Solutions

What is the difference between a statistic and a parameter?

Use the Central Limit Theorem to explain the difference between the sampling distribution of x and...

Use the Central Limit Theorem to explain the difference between the sampling distribution of x and x-bar.

This week we’ve introduced the central limit theorem. According to the central limit theorem, for all...

This week we’ve introduced the central limit theorem. According to the central limit theorem, for all samples of the same size n with n>30, the sampling distribution of x can be approximated by a normal distribution. In your initial post use your own words to explain what this theorem means. Then provide a quick example to explain how this theorem might apply in real life. At last, please share with us your thoughts about why this theorem is important.

explain the difference between a parameter and statistic using an example

a) Explain the difference between a statistic and a parameter. b) Is it possible to calculate...

a) Explain the difference between a statistic and a parameter. b) Is it possible to calculate a parameter? Why or why not? c) Can you trust a statistic? Explain. d) Will statistics change from one sample to another? Why or why not?

What is the central limit theorem? What is the standard error of the mean?

Question 5 What is the central limit theorem? Provide examples. Question 6 What is the standard error of the mean? Provide examples.

describe what the central limit theorem is, and list the requirements necessary for using the Central...

describe what the central limit theorem is, and list the requirements necessary for using the Central Limit Theorem.

It is said that the Central Limit Theorem is the most important theorem in all of...

It is said that the Central Limit Theorem is the most important theorem in all of Statistics. In your own words, describe why it is so important.

What is the Central Limit Theorem? Discuss an example of its application.

What is the sampling distribution of the sample mean? What is the central limit theorem?

Question 4 What is the sampling distribution of the sample mean? Provide examples. Question 5 What is the central limit theorem? Provide examples. Question 6 What is the standard error of the mean? Provide examples.

Subjects