Question

1. What does “distribution sampling” reveal to the researcher.
2. Give an example of how “distribution sampling” is used in a real-world scenario.

Write a one to two (1–2) page short paper in which you answer the questions about distribution sampling.

Answer 1

Def :

A sampling distribution is a probability distribution of a statistic obtained through a large number of samples drawn from a specific population. The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population.

A sampling distribution is a graph of a statistic for your sample data.

• Mean
• Mean absolute value of the deviation from the mean
• Range
• Standard deviation of the sample
• Unbiased estimate of variance
• Variance of the sample

For any given sample size (even n = 2) we would say that the sample mean (from the two people) estimates the population mean. But the estimation accuracy -- that is, how good a job we've done of estimating the population mean based on our sample data, as reflected in the standard error of the mean -- will be poorer than if we had a 20 or 200 people in our sample. This is relatively intuitive (larger samples give better estimation accuracy).

We would then use the standard error to calculate a confidence interval, which (in this case) is based around the Normal distribution (we'd probably use the t-distribution in small samples since the standard deviation of the population is often underestimated in a small sample, leading to overly optimistic standard errors.)

In simple terms, sampling is the process of selection of limited number of elements from large group of elements (population) so that, the characteristics of the samples taken is identical to that of the population. In above examples, suppose you choose 1000 students among 4 millions students. then:

• 4 millions students is population
• 1000 is the size of sample

Sampling is a great tool if you have to deal with a huge volume of data and you have limited resources. When you have large population of the data, then it can also be the only option you have.

Advantages of Sampling:

Sampling have various benefits to us. Some of the advantages are listed below:

• Sampling saves time to a great extent by reducing the volume of data. You do not go through each of the individual items.
• Sampling Avoids monotony in works. You do not have to repeat the query again and again to all the individual data.
• When you have limited time, survey without using sampling becomes impossible. It allows us to get near-accurate results in much lesser time
• When you use proper methods, you are likely to achieve higher level of accuracy by using sampling than without using sampling in some cases due to reduction in monotony, data handling issues etc.
• By using sampling, you can get detailed information on the data even by employing small amount of resources.

Real life example. Suppose you want to study election exit poll. So it is sampling distribution is very useful for this..