## Important Sampling and Sampling Distribution MCQs – 5

The Online Sampling and Sampling Distribution MCQs for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. These tests are also helpful in getting admission to different colleges and Universities. Most of the questions in this quiz Sampling and Sampling Distribution MCQs cover the topics of Probability Sampling and Non-Probability Sampling, Mean and Standard Deviation of Sample, Sample size, Sampling error, Sample bias, Sample Selection, etc.

The sampling Quiz on this page is covered from Sampling and Sampling Distributions, Probability Sampling and Non-Probability Sampling, Mean and Standard Deviation of Sample, Sample size, Sampling error, Sample bias, and Sample Selection, etc.

1. Suppose a finite population contains 7 items and 3 items are selected at random without replacement, then all possible samples will be

2. In sampling with replacement, the standard error of $\overline{X}$ is equal to

3. The finite population correction factor is ________

4. Which of the following are examples of sampling bias? Select all that apply.

5. Suppose a finite population contains 4 items and 2 items are selected at random with replacement, then how many samples will be there

6. A plan for obtaining a sample from a population is called

7. If $N$ is the size of the population and $n$ is the sample size, then the sampling fraction is ______

8. Suppose a population has $N$ items and $n$ items are selected with replacement. The number of all possible samples will be

9. In sampling without replacement, an element can be chosen

10. What concept states that the sampling distribution of the mean approaches a normal distribution as the sample size increases?

11. In sampling without replacement, an element can be chosen

12. A population contains $N$ items and all possible samples of size $n$ are selected without replacement. The possible number of samples will be

13. Which one of the following is the main problem with using non-probability sampling techniques?

14. Suppose a finite population has 6 items and 2 items are selected at random without replacement, then all possible samples will be

15. Sampling error is reduced by _______

16. In random sampling, the probability of selecting an item from the population is ______

17. Non-random sampling is also called ________

18. Random Sampling is also called _______

19. A population contains 2 items and 4 items are selected at random with replacement, then all possible samples will be

20. In sampling with replacement, the following is always true __________

The sampling Quiz is about the Basics of Sampling and Sampling Distribution MCQs. It will help you understand the basic concepts of sampling methods and distributions. This test will also help you prepare for different exams related to education or jobs.

### Sampling and Sampling Distribution MCQs

• In sampling without replacement, an element can be chosen
• In sampling with replacement, the following is always true _________
• Suppose a finite population has 6 items and 2 items are selected at random without replacement, then all possible samples will be
• Suppose a finite population contains 7 items and 3 items are selected at random without replacement, then all possible samples will be
• A population contains $N$ items and all possible samples of size $n$ are selected without replacement. The possible number of samples will be
• Suppose a finite population contains 4 items and 2 items are selected at random with replacement, then how many samples will be there
• A population contains 2 items and 4 items are selected at random with replacement, then all possible samples will be
• Suppose a population has $N$ items and $n$ items are selected with replacement. The number of all possible samples will be
• In random sampling, the probability of selecting an item from the population is _________.
• Random Sampling is also called ___________.
• Non-random sampling is also called
• Sampling error is reduced by
• If $N$ is the size of the population and $n$ is the sample size, then the sampling fraction is ________
• The finite population correction factor is ____________
• In sampling with replacement, the standard error of $\overline{X}$ is equal to
• What concept states that the sampling distribution of the mean approaches a normal distribution as the sample size increases?
• Which of the following are examples of sampling bias? Select all that apply.
• A plan for obtaining a sample from a population is called
• In sampling without replacement, an element can be chosen
• Which one of the following is the main problem with using non-probability sampling techniques?

The important points about Sampling and Sampling Distributions are:

• The shape of the sampling distribution depends on the underlying population distribution, the statistic being calculated (mean, median, etc.), and the sample size.
• Larger sample sizes tend to produce sampling distributions that are more normally distributed (bell-shaped and symetrical), regardless of the population distribution due to the Central Limit Theorem.
• The sampling distribution is used to make inferences about the population from which the sample was drawn. For example, we can estimate the population mean by looking at the average of the sample means from many samples.

By understanding the concepts and theories about sampling and sampling distributions, one can make informed decisions based on the data collected from samples, even though one can not easily study the entire population.

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## Best Sampling Quiz – 4

The sampling Quiz in this post covers the MCQs related to Sampling and Sampling Distributions, Probability Sampling and Non-Probability Sampling, Mean and Standard Deviation of Sample, Sample size, Sampling error, Sample bias, Sample Selection, etc.

Please go to Best Sampling Quiz – 4 to view the test

### Online Sampling Quiz with Answers

• A sample is a subset of
• A list of all the units of the population is called
• Any calculation on the sample data is called
• Any measure of the population is called
• The difference between a statistic and the parameter is called
• The probability distribution of a statistic is called
• The standard deviation of the sampling distribution of a statistic is called
• If we obtain a point estimate $\overline{X}$ for a population mean $\mu$, the difference between $\overline{X}$ and $\mu$ is called
• A distribution formed by all possible values of a statistic is called
• In probability sampling, the probability of selecting an item from the population is known and is
• A population about which we want to get some information is called
• The study of population is called
• To make a voter list in Pakistan we need
• Sampling-based on equal probability is called
• In sampling with replacement, an element can be chosen
• A company is trying to learn more about their customer base. They would like to survey to understand why their customers chose their brand. How should the company survey its customers?
• A high school principal is estimating the total number of students that will attend an upcoming event. She assumes that the older students are unlikely to attend and decides to only survey the first-year students. What issue will the principal face when calculating her estimation?
• A clothing manufacturer wants to learn more about why their consumers have purchased the brand’s products. How should this manufacturer conduct their survey?
• What is a standard error?
• A data professional is analyzing data about a population of aspen trees. They take repeated random samples of 10 trees from the population and compute the mean height for each sample. Which of the following statements best describes the sampling distribution of the mean?

The sampling Quiz is about the Basics of Sampling and Sampling Distributions. It will help you to understand the basic concepts related to sampling methods and sampling distributions. This test will also help you to prepare yourself for different exams related to education or jobs.

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## Probability and Non-Probability Sampling (2021)

The fundamental methods of Probability and non-probability sampling are used for selecting a sample from a population in research studies. They differ in how they approach the selection process and the resulting generalizability of the findings. The non-probability sampling methods are valuable for initial research stages or specific situations, but for strong statistical inferences and generalizability, probability sampling is preferred.

In probability sampling, each unit of the population has a known (non-zero) probability of being included in the sample, and samples are selected randomly by using some random selection method. That’s why probability sampling may also be called random sampling. In probability sampling, the reliability of the estimates can be determined. In probability sampling, samples are selected without any interest. The advantage of probability sampling is that it provides a valid estimate of sampling error. Probability sampling is widely used in various areas such as industry, agriculture, business sciences, etc.

Important types of probability sampling are

• Simple Random Sampling
• Stratified Random Sampling
• Systematic Sampling
• Cluster Sampling

Non-probability sampling

In this sampling technique samples are selected by personal judgment due to this personal judgment in the selection of sample bias may include which makes the result unrepresentative. This sampling technique may also be called non-random sampling. The disadvantage of non-probability is that the reliability of the estimates cannot be determined.

The Non-Probability Samplings are:

• Purposive sampling
• Quota sampling
• Judgment sampling
• Snowball sampling
• Convenience sampling

### Differences between Probability and Non-Probability Sampling

The difference between these two is that non-probability sampling does not involve random selection of objects while in probability sampling objects are selected by using some random selection method. In other words, it means that non-probability samples aren’t representative of the population, but it is not necessary. However, it may mean that non-probability samples cannot depend upon the rationale of probability theory.

In general, researchers may prefer probabilistic or random sampling methods over a non-probabilistic sampling method, and consider them to be more accurate and rigorous.  However, in applied social sciences, for researchers, there may be circumstances where it is not possible to obtain sampling using some probability sampling methods. Even practical or theoretical it may not be sensible to do random sampling. Therefore a wide range of non-probability sampling methods may be considered, in these circumstances.

The choice between probability and non-probability sampling depends on the research question, resources available, and the desired level of generalizability.

• Use probability sampling when the generalizability of findings to the population is crucial, and resources allow for random selection.
• Use non-probability sampling when: You need a quick and easy way to gather initial insights, explore a topic, or a complete sampling frame is unavailable. However, be cautious about generalizing the results.

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## Sampling Basics and Objectives (2021)

In this article, we will discuss the Sampling Basics. It is often required to collect information from the data. These two methods are used for collecting the required information.

• Complete information
• Sampling

### Complete Information

This method collects the required information from every individual in the population. This method is used when it is difficult to draw some conclusion (inference) about the population based on sample information. This method is costly and time-consuming. This method of getting data is also called Complete Enumeration or Population Census.

### Sampling Basics

#### What is Sampling?

Sampling is the most common and widely used method of collecting information. Instead of studying the whole population only a small part of the population is selected and studied and the result is applied to the whole population. For example, a cotton dealer picked up a small quantity of cotton from the different bales to know the quality of the cotton.

#### Purpose or objective of sampling

Two basic purposes of sampling are

1. To obtain the maximum information about the population without examining every unit of the population.
2. To find the reliability of the estimates derived from the sample, which can be done by computing the standard error of the statistic.

### Advantages of sampling over Complete Enumeration

1. It is a much cheaper method to collect the required information from the sample as compared to complete enumeration as fewer units are studied in the sample rather than the population.
2. From a sample, the data can be collected more quickly and greatly save time.
3. Planning for sample surveys can be done more carefully and easily as compared to complete enumeration.
4. Sampling is the only available method of collecting the required information when the population object/ subject or individual in the population is destructive.
5. Sampling is the only available method of collecting the required information when the population is infinite or large enough.
6. The most important advantage of sampling is that it provides the reliability of the estimates.
7. Sampling is extensively used to obtain some of the census information.

This is all about Sampling Basics.