CAIIB ABM Module A Unit 2 MCQs – Best 100 MCQs

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CAIIB ABM Module A Unit 2 MCQs – Best 100 MCQs to crack CAIIB Exam in first attempt.

 Sampling Fundamentals – CAIIB ABM Module A Unit 2 MCQs

Question 1: What is the primary goal of sampling from a population?

A. To collect data from every individual in the population.

B. To make inferences about the population based on a subset of data.

C. To ensure that every sample has an equal chance of being selected.

D. To reduce the cost of data collection.

Answer
Answer  : B. The main purpose of sampling is to gather information from a representative portion of the population, allowing us to draw conclusions about the entire population without having to collect data from every single individual.

Question 2: Which of the following best describes a representative sample?

A. A sample that is selected randomly.

B. A sample that includes individuals from all segments of the population.

C. A sample that accurately reflects the characteristics of the population.

D. A sample that is large enough to provide statistically significant results.

Answer
Answer  : C. A representative sample is one that mirrors the key attributes and characteristics of the larger population it is drawn from, allowing for generalizations to be made about the population.

Question 3: What is the trade-off between sample size and accuracy in sampling?

A. Larger samples are always more accurate.

B. Smaller samples are always more cost-effective.

C. Larger samples generally lead to increased accuracy but also higher costs.

D. There is no relationship between sample size and accuracy.

Answer
Answer  : C. Increasing the sample size typically improves the accuracy of estimates but also increases the cost and time associated with data collection and analysis. The optimal sample size balances the need for accuracy with practical constraints.

Question 4: Which of the following is NOT a reason for using sampling instead of a census (complete enumeration)?

A. Cost savings

B. Time savings

C. Improved accuracy

D. Practicality when dealing with large populations

Answer
Answer  : C. While sampling can be very accurate, a census, by definition, provides the most accurate information about a population because it involves collecting data from every individual. However, censuses are often impractical due to their high cost, time commitment, and logistical challenges.

Question 5: In which of the following scenarios would sampling be most appropriate?

A. Determining the average age of students in a small classroom.

B. Estimating the proportion of defective products in a large manufacturing batch.

C. Calculating the total number of employees in a company.

D. Conducting a survey to understand the political opinions of every citizen in a country.

Answer
Answer  : B. Sampling is particularly useful when dealing with large populations or when it is impractical or impossible to collect data from every individual. Estimating the proportion of defective products in a large manufacturing batch is a typical example where sampling is used to ensure quality control without having to inspect every single product.

CAIIB ABM Module A Unit 2 MCQs

Question 6: What is the relationship between the sample size and the margin of error in a survey?

A. The larger the sample size, the larger the margin of error.

B. The smaller the sample size, the smaller the margin of error.

C. The larger the sample size, the smaller the margin of error.

D. There is no relationship between sample size and margin of error.

Answer
Answer  : C. The margin of error is a measure of the uncertainty associated with an estimate. Larger sample sizes generally lead to more precise estimates and, therefore, a smaller margin of error.

Question 7: Which of the following is a potential disadvantage of using a small sample size?

A. Increased accuracy

B. Reduced cost

C. Higher margin of error

D. Faster data collection

Answer
Answer  : C. Smaller sample sizes are more susceptible to random fluctuations and may not accurately represent the entire population, leading to a higher margin of error and less reliable estimates.

Question 8: What is the main purpose of ensuring representativeness in sampling?

A. To make the sample easier to collect.

B. To reduce the cost of data collection.

C. To allow for generalizations about the population based on the sample.

D. To ensure that every individual in the population has an equal chance of being selected.

Answer
Answer  : C. Representativeness is crucial in sampling because it allows researchers to confidently generalize the findings from the sample to the larger population from which it was drawn.

Question 9: Which of the following factors does NOT influence the required sample size for a study?

A. Desired level of accuracy

B. Variability within the population

C. Type of sampling method used

D. The researcher’s personal preference

Answer
Answer  : D. The researcher’s personal preference does not play a role in determining the appropriate sample size. The required sample size is determined by factors such as the desired level of accuracy, the variability within the population being studied, and the specific sampling method employed.

Question 10: What is the key principle behind the concept of sampling?

A. Every individual in the population must be included in the sample.

B. The sample should be as large as possible to ensure accuracy.

C. The sample should be selected in a way that allows for generalizations about the population.

D. Sampling is only useful in situations where a census is impossible.

Answer
Answer  : C. The fundamental principle of sampling is to select a subset of individuals from a population in a manner that allows the sample to accurately represent the characteristics of the entire population, enabling researchers to draw meaningful conclusions about the population based on the sample data.

CAIIB ABM Module A Unit 2 MCQs

CAIIB ABM Module A Unit 2 MCQs - Best 100 MCQs

 Sampling Distributions – CAIIB ABM Module A Unit 2 MCQs

Question 11: What is a sampling distribution?

A. The distribution of all possible values of a statistic from all possible samples of a particular size drawn from the population.

B. The distribution of individuals in a population.

C. The distribution of a single sample from a population.

D. The distribution of all possible populations.

Answer
Answer  : A. A sampling distribution is a theoretical distribution that shows all the possible values a statistic (like the mean or proportion) can take when calculated from different samples of the same size drawn from a population.

Question 12: What is the role of sampling distributions in statistical inference?

A. They help us understand the variability of sample statistics.

B. They allow us to make inferences about population parameters based on sample statistics.

C. They provide a visual representation of the data collected from a sample.

D. Both A and B.

Answer
Answer  : D. Sampling distributions are essential in statistical inference because they help us understand how much sample statistics are likely to vary due to random chance. This understanding allows us to make informed inferences about population parameters based on the observed sample data.

Question 13: Which of the following is NOT a type of sampling distribution?

A. Sampling distribution of the mean

B. Sampling distribution of the median

C. Sampling distribution of the mode

D. Sampling distribution of the standard deviation

Answer
Answer  : C. While there are sampling distributions for the mean, median, and standard deviation, there is no specific sampling distribution for the mode.

Question 14: What happens to the sampling distribution of the mean as the sample size increases?

A. It becomes more spread out.

B. It becomes less spread out.

C. It remains the same.

D. It becomes bimodal.

Answer
Answer  : B. As the sample size increases, the sampling distribution of the mean becomes less spread out (i.e., its standard deviation decreases). This is because larger samples tend to provide more precise estimates of the population mean.

Question 15: What is the standard error of the mean?

A. The standard deviation of the population.

B. The standard deviation of a single sample.

C. The standard deviation of the sampling distribution of the mean.

D. The mean of the sampling distribution of the mean.

Answer
Answer  : C. The standard error of the mean is the standard deviation of the sampling distribution of the mean. It quantifies how much the sample means are expected to vary from the true population mean due to random sampling error.

CAIIB ABM Module A Unit 2 MCQs

Question 16: Which of the following statements is true about the relationship between sample size and standard error?

A. As the sample size increases, the standard error increases.

B. As the sample size increases, the standard error decreases.

C. The sample size has no effect on the standard error.

D. The relationship between sample size and standard error depends on the population distribution.

Answer
Answer  : B. The standard error of the mean is inversely proportional to the square root of the sample size. This means that as the sample size increases, the standard error decreases, indicating greater precision in estimating the population mean.

Question 17: What is the mean of the sampling distribution of the mean?

A. It is always equal to the population mean.

B. It is always equal to the sample mean.

C. It is always greater than the population mean.

D. It is always less than the population mean.

Answer
Answer  : A. The mean of the sampling distribution of the mean is always equal to the population mean. This is a fundamental property of sampling distributions.

Question 18: Which of the following is an example of a sampling distribution?

A. The distribution of heights of all students in a school.

B. The distribution of ages of a random sample of 50 people.

C. The distribution of average salaries calculated from multiple random samples of 30 employees each.

D. The distribution of genders in a population.

Answer
Answer  : C. A sampling distribution is the distribution of a statistic (like the mean) calculated from multiple samples. Option C describes the distribution of average salaries calculated from multiple samples, making it an example of a sampling distribution.

Question 19: Why are sampling distributions important in hypothesis testing?

A. They help us determine the likelihood of obtaining a sample statistic as extreme as or more extreme than the one observed, assuming the null hypothesis is true.

B. They tell us the exact value of the population parameter.

C. They provide a visual representation of the sample data.

D. They are not used in hypothesis testing.

Answer
Answer  : A. Sampling distributions play a crucial role in hypothesis testing by providing a framework for assessing the probability of observing a sample statistic under the assumption that the null hypothesis is true. This probability helps us decide whether to reject or fail to reject the null hypothesis.

Question 20: Which of the following is an advantage of using sampling distributions?

A. They eliminate the need for collecting data from the entire population.

B. They provide a way to quantify the uncertainty associated with sample statistics.

C. They allow us to make inferences about population parameters.

D. All of the above.

Answer
Answer  : D. Sampling distributions offer several advantages, including enabling inferences about populations based on samples, quantifying the uncertainty in sample estimates, and making data collection more efficient by avoiding the need for a census.

CAIIB ABM Module A Unit 2 MCQs

 Sampling from Normal and Non-Normal Populations – CAIIB ABM Module A Unit 2 MCQs

Question 21: If the population distribution is normal, what is the shape of the sampling distribution of the mean?

A. Normal, regardless of the sample size

B. Normal, only if the sample size is large (n ≥ 30)

C. Non-normal, regardless of the sample size

D. The shape depends on the population standard deviation

Answer
Answer  : A. If the population distribution is normal, the sampling distribution of the mean will also be normal, regardless of the sample size.

Question 22: If the population distribution is non-normal, what happens to the shape of the sampling distribution of the mean as the sample size increases?

A. It becomes more non-normal.

B. It approaches a normal distribution.

C. It remains non-normal.

D. It becomes uniform.

Answer
Answer  : B. The Central Limit Theorem states that even if the population distribution is non-normal, the sampling distribution of the mean will tend to approach a normal distribution as the sample size increases.

Question 23: What is the minimum sample size typically required for the Central Limit Theorem to apply effectively?

A. 10

B. 20

C. 30

D. 50

Answer
Answer  : C. While the sampling distribution of the mean can approach normality with smaller sample sizes, a general rule of thumb is that a sample size of at least 30 is sufficient for the Central Limit Theorem to be applied effectively.

Question 24: Which of the following is NOT an assumption of the Central Limit Theorem?

A. The sample is drawn from a normally distributed population.

B. The sample is drawn randomly.

C. The sample size is sufficiently large.

D. The observations in the sample are independent.

Answer
Answer  : A. The Central Limit Theorem does not require the population distribution to be normal. It applies even when the population distribution is non-normal, as long as the other assumptions are met.

Question 25: What is the implication of the Central Limit Theorem for statistical inference?

A. It allows us to use normal distribution-based methods even when the population distribution is not normal, provided the sample size is large enough.

B. It guarantees that the sample mean will always be equal to the population mean.

C. It eliminates the need for sampling altogether.

D. It only applies to populations that are perfectly normally distributed.

Answer
Answer  : A. The Central Limit Theorem is a powerful tool in statistical inference because it allows us to use the well-understood normal distribution to make inferences about population parameters even when the underlying population distribution is not normal, as long as the sample size is sufficiently large.

CAIIB ABM Module A Unit 2 MCQs

Question 26: Which of the following statements is true about sampling from a non-normal population?

A. The sampling distribution of the mean will always be non-normal.

B. The sampling distribution of the mean will be normal only if the sample size is very large.

C. The sampling distribution of the mean will approach normality as the sample size increases, even if the population is not normal.

D. The Central Limit Theorem does not apply to non-normal populations.

Answer
Answer  : C. The Central Limit Theorem states that the sampling distribution of the mean will tend towards a normal distribution as the sample size increases, regardless of the shape of the original population distribution.

Question 27: What is the impact of a larger sample size when sampling from a non-normal population?

A. It makes the sampling distribution of the mean more non-normal.

B. It slows down the convergence of the sampling distribution of the mean towards normality.

C. It accelerates the convergence of the sampling distribution of the mean towards normality.

D. It has no impact on the shape of the sampling distribution of the mean.

Answer
Answer  : C. Larger sample sizes lead to a faster convergence of the sampling distribution of the mean towards a normal distribution, even when the population distribution is not normal.

Question 28: In which of the following scenarios would the Central Limit Theorem be most useful?

A. Estimating the average height of students in a classroom where the heights are normally distributed.

B. Estimating the proportion of defective items in a small batch of products.

C. Estimating the average income of a large population where the income distribution is skewed.

D. Calculating the exact value of the population mean.

Answer
Answer  : C. The Central Limit Theorem is particularly valuable when dealing with large populations that have non-normal distributions. It allows us to use normal distribution-based methods to make inferences about the population mean even when the underlying distribution is skewed or non-normal.

Question 29: Which of the following is an example of a non-normal population distribution?

A. The distribution of heights of adult males.

B. The distribution of scores on a standardized test.

C. The distribution of incomes in a country.

D. The distribution of weights of a random sample of 100 people.

Answer
Answer  : C. Income distributions are often skewed, with a long tail on the right side representing high-income earners. This makes them an example of a non-normal population distribution.

Question 30: What is the significance of the shape of the population distribution in sampling?

A. It determines the shape of the sampling distribution, regardless of the sample size.

B. It has no impact on the shape of the sampling distribution.

C. It influences the shape of the sampling distribution, especially for small sample sizes.

D. It only matters if the population distribution is perfectly normal.

Answer
Answer  : C. The shape of the population distribution can influence the shape of the sampling distribution, particularly when the sample size is small. However, as the sample size increases, the Central Limit Theorem ensures that the sampling distribution of the mean approaches normality, regardless of the shape of the population distribution.

CAIIB ABM Module A Unit 2 MCQs

 Central Limit Theorem – CAIIB ABM Module A Unit 2 MCQs

Question 31: What is the Central Limit Theorem?

A. A theorem that states that the sampling distribution of the mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.

B. A theorem that states that the sample mean is always equal to the population mean.

C. A theorem that states that all populations are normally distributed.

D. A theorem that states that sampling is unnecessary for statistical inference.

Answer
Answer  : A. The Central Limit Theorem is a fundamental concept in statistics that describes the behavior of the sampling distribution of the mean as the sample size increases. It states that regardless of the shape of the original population distribution, the sampling distribution of the mean will tend towards a normal distribution as the sample size gets larger.

Question 32: What is the significance of the Central Limit Theorem in statistical inference?

A. It allows us to make inferences about population parameters based on sample statistics, even if the population distribution is not normal.

B. It guarantees that the sample mean will always be equal to the population mean.

C. It eliminates the need for sampling altogether.

D. It only applies to populations that are perfectly normally distributed.

Answer
Answer  : A. The Central Limit Theorem is crucial in statistical inference because it enables us to use the well-understood normal distribution to make inferences about population parameters even when the underlying population distribution is not normal, provided the sample size is sufficiently large. This greatly expands the applicability of statistical methods.

Question 33: Which of the following conditions is necessary for the Central Limit Theorem to apply?

A. The population distribution must be normal.

B. The sample size must be small.

C. The sample must be drawn randomly.

D. The observations in the sample must be dependent.

Answer
Answer  : C. The Central Limit Theorem requires that the sample be drawn randomly from the population. This ensures that each individual in the population has an equal chance of being included in the sample, which is essential for making valid inferences about the population.

Question 34: What happens to the shape of the sampling distribution of the mean as the sample size increases, according to the Central Limit Theorem?

A. It becomes more skewed.

B. It becomes more uniform.

C. It approaches a normal distribution.

D. It becomes bimodal.

Answer
Answer  : C. The Central Limit Theorem states that as the sample size increases, the sampling distribution of the mean will tend to approximate a normal distribution, regardless of the shape of the original population distribution.

Question 35: The Central Limit Theorem states that the sampling distribution of the mean will approach a normal distribution as the sample size increases. Does this apply even if the population distribution is not normal?

A. No, the Central Limit Theorem only applies when the population distribution is normal.

B. Yes, the Central Limit Theorem applies regardless of the shape of the population distribution.

C. The Central Limit Theorem only applies when the sample size is very large (n > 100).

D. The Central Limit Theorem only applies when the population distribution is symmetrical.

Answer
Answer  : B. The Central Limit Theorem is a powerful statistical concept that holds true even when the population distribution is not normally distributed. As the sample size increases, the distribution of sample means will tend towards a normal distribution, regardless of the shape of the original population.

CAIIB ABM Module A Unit 2 MCQs

Question 36: Which of the following is NOT an implication of the Central Limit Theorem?

A. We can use the normal distribution to approximate the sampling distribution of the mean for large sample sizes, even if the population is not normally distributed.

B. The mean of the sampling distribution of the mean is equal to the population mean.

C. The standard deviation of the sampling distribution of the mean (standard error) decreases as the sample size increases.

D. The Central Limit Theorem guarantees that the sample mean will always be equal to the population mean.

Answer
Answer  : D. The Central Limit Theorem does not guarantee that the sample mean will always be equal to the population mean. It states that the distribution of sample means will tend towards a normal distribution centered around the population mean, but individual sample means can still vary due to random sampling error.

Question 37: In which of the following scenarios is the Central Limit Theorem most likely to be applicable?

A. A small sample (n = 10) is drawn from a normally distributed population.

B. A large sample (n = 100) is drawn from a population with an unknown distribution.

C. A small sample (n = 15) is drawn from a heavily skewed population.

D. The Central Limit Theorem applies to all scenarios, regardless of sample size or population distribution.

Answer
Answer  : B. The Central Limit Theorem is most applicable when dealing with large samples drawn from populations with unknown distributions. The larger the sample size, the more likely it is that the sampling distribution of the mean will approximate a normal distribution, even if the population distribution is not normal.

Question 38: What is the practical significance of the Central Limit Theorem for researchers and analysts?

A. It simplifies the process of statistical inference by allowing the use of normal distribution-based methods even when the population distribution is not normal.

B. It eliminates the need for random sampling.

C. It guarantees that all sample means will be identical to the population mean.

D. It is only relevant in theoretical statistics and has no practical applications.

Answer
Answer  : A. The Central Limit Theorem has significant practical implications because it allows researchers and analysts to use the familiar and well-understood normal distribution to make inferences about population parameters, even when the underlying population distribution is not normal. This simplifies the process of statistical analysis and hypothesis testing.

Question 39: Which of the following statements accurately describes the relationship between the Central Limit Theorem and sample size?

A. The Central Limit Theorem applies only to very large sample sizes (n > 1000).

B. The Central Limit Theorem is more likely to hold true for larger sample sizes.

C. The Central Limit Theorem is equally applicable to all sample sizes.

D. The Central Limit Theorem is less likely to hold true for larger sample sizes.

Answer
Answer  : B. While the Central Limit Theorem can sometimes apply to smaller sample sizes, it is generally more likely to hold true and provide accurate approximations for larger sample sizes. As the sample size increases, the sampling distribution of the mean converges more closely to a normal distribution.

Question 40: How does the Central Limit Theorem help in estimating population parameters?

A. It provides a direct formula for calculating the exact value of any population parameter.

B. It allows us to use sample statistics and the normal distribution to make inferences about population parameters, even when the population distribution is unknown.

C. It eliminates the need for any statistical analysis.

D. It is only useful for estimating the population mean, not other parameters.

Answer
Answer  : B. The Central Limit Theorem enables us to leverage sample statistics and the properties of the normal distribution to estimate population parameters, such as the mean and proportion, even when we don’t have complete information about the underlying population distribution. This is a cornerstone of statistical inference.

CAIIB ABM Module A Unit 2 MCQs

 Finite Population Multiplier – CAIIB ABM Module A Unit 2 MCQs

Question 41: When is the finite population multiplier used in sampling?

A. When sampling from a finite population with replacement

B. When sampling from an infinite population

C. When sampling from a finite population without replacement

D. It is always used, regardless of the population size or sampling method

Answer
Answer  : C. The finite population multiplier is a correction factor applied to the standard error of the mean when sampling from a finite population without replacement. It accounts for the fact that as you sample more items from a finite population, the remaining population size decreases, affecting the variability of subsequent samples.

Question 42: What is the purpose of the finite population multiplier?

A. To increase the standard error of the mean

B. To decrease the standard error of the mean

C. To adjust the standard error of the mean when sampling from a finite population without replacement

D. To estimate the population size

Answer
Answer  : C. The finite population multiplier is used to correct the standard error of the mean when sampling without replacement from a finite population. It ensures that the standard error accurately reflects the reduced variability in the remaining population as more items are sampled.

Question 43: What happens to the finite population multiplier as the sample size (n) approaches the population size (N)?

A. It approaches 0

B. It approaches 1

C. It remains constant

D. It becomes undefined

Answer
Answer  : A. As the sample size (n) gets closer to the population size (N), the finite population multiplier approaches 0. This is because when you sample a large proportion of the population, the remaining unsampled portion becomes smaller, leading to reduced variability in subsequent samples.

Question 44: In which of the following situations is the finite population multiplier most likely to have a significant impact on the standard error of the mean?

A. When the population size is very large compared to the sample size

B. When the sample size is very small compared to the population size

C. When the population size is equal to the sample size

D. The finite population multiplier always has a significant impact, regardless of the population and sample sizes

Answer
Answer  : C. The finite population multiplier has the most significant impact when the sample size is a substantial proportion of the population size. In such cases, the correction factor significantly reduces the standard error, reflecting the decreased variability in the remaining population.

Question 45: What is the formula for the finite population multiplier?

A. √(N – n) / (N – 1)

B. √(n – N) / (N – 1)

C. √(N – 1) / (N – n)

D. √(N – 1) / (n – N)

Answer
Answer  : A. The finite population multiplier is calculated as the square root of (N – n) divided by (N – 1), where N is the population size and n is the sample size.

CAIIB ABM Module A Unit 2 MCQs

Question 46: Which of the following statements is true about the relationship between the finite population multiplier and the standard error of the mean?

A. The finite population multiplier is added to the standard error of the mean.

B. The finite population multiplier is subtracted from the standard error of the mean.

C. The finite population multiplier is multiplied by the standard error of the mean.

D. The finite population multiplier has no relationship with the standard error of the mean.

Answer
Answer  : C. The finite population multiplier is used as a correction factor that is multiplied by the standard error of the mean calculated for an infinite population or sampling with replacement. This adjustment accounts for the finite population size and sampling without replacement.

Question 47: When can the finite population multiplier be ignored in practice?

A. When the sample size is very large

B. When the population size is very small

C. When the sampling fraction (n/N) is less than 0.05

D. It should never be ignored

Answer
Answer  : C. As a general rule of thumb, the finite population multiplier can be safely ignored when the sampling fraction (the ratio of the sample size to the population size) is less than 0.05. In such cases, the impact of the finite population correction on the standard error is negligible.

Question 48: What is the sampling fraction?

A. The ratio of the sample size to the population size (n/N)

B. The ratio of the population size to the sample size (N/n)

C. The difference between the population size and the sample size (N – n)

D. The sum of the population size and the sample size (N + n)

Answer
Answer  : A. The sampling fraction is calculated by dividing the sample size (n) by the population size (N). It represents the proportion of the population that is included in the sample.

Question 50: The finite population multiplier is 0.95. If the standard error of the mean calculated assuming an infinite population is 5, what is the corrected standard error considering the finite population?

A. 4.75

B. 5.25

C. 5

D. 0.25

Answer
Answer  : A. The corrected standard error is calculated by multiplying the finite population multiplier with the standard error assuming an infinite population. So, 0.95 * 5 = 4.75

CAIIB ABM Module A Unit 2 MCQs

 Types of Sampling – CAIIB ABM Module A Unit 2 MCQs

Question 51: Which of the following is NOT a type of random sampling?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Convenience sampling

Answer
Answer  : D. Convenience sampling is a non-probability sampling method where the sample is selected based on ease of access, rather than random selection. The other options are all types of random or probability sampling.

Question 52: In which type of sampling does every member of the population have an equal chance of being selected?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : A. In simple random sampling, each member of the population has an equal and independent chance of being included in the sample. This is the most basic form of probability sampling.

Question 53: Which sampling method involves selecting every kth element from a list after a random start?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : B. Systematic sampling involves selecting individuals from an ordered list at regular intervals (every kth element) after a random starting point. This method is often more convenient than simple random sampling.

Question 54: Which sampling method is most appropriate when the population is divided into homogeneous groups and you want to ensure representation from each group?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : C. Stratified sampling is used when the population is divided into distinct subgroups or strata, and you want to ensure that each stratum is adequately represented in the sample. This method helps improve the precision and representativeness of the sample.

Question 55: Which sampling method involves dividing the population into clusters and then randomly selecting entire clusters to be included in the sample?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : D. Cluster sampling involves dividing the population into clusters or groups and then randomly selecting some of these clusters to be included in the sample. All individuals within the selected clusters are then included in the sample. This method can be cost-effective when dealing with geographically dispersed populations.

CAIIB ABM Module A Unit 2 MCQs

Question 56: Which of the following is an example of judgment sampling?

A. Selecting every 10th student from a school roster.

B. Dividing a city into blocks and randomly selecting some blocks for a survey.

C. Choosing participants for a focus group based on their expertise and experience.

D. Using a random number generator to select survey respondents.

Answer
Answer  : C. Judgment sampling, also known as purposive sampling, involves selecting individuals for the sample based on the researcher’s judgment and knowledge about the population. This method is often used in qualitative research or when specific expertise is required.

Question 57: What is the main advantage of random sampling over non-random sampling?

A. It is easier to implement.

B. It is less expensive.

C. It allows for statistical inference and generalization to the population.

D. It guarantees that the sample will perfectly represent the population.

Answer
Answer  : C. The primary advantage of random sampling is that it allows for statistical inference, meaning that you can use the sample data to make generalizations and draw conclusions about the larger population from which the sample was drawn. This is not possible with non-random sampling methods.

Read Also: CAIIB ABM Unit 1 MCQs – Best 100 MCQs

Question 58: Which sampling method is most suitable when dealing with a large, geographically dispersed population?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : D. Cluster sampling is often preferred when dealing with large and geographically dispersed populations because it can be more cost-effective than other methods. By randomly selecting clusters or groups of individuals, researchers can reduce travel costs and logistical challenges associated with data collection.

Question 59: What is the main disadvantage of judgment sampling?

A. It is time-consuming.

B. It is expensive.

C. It can introduce bias into the sample.

D. It is not suitable for small populations.

Answer
Answer  : C. The main drawback of judgment sampling is that it relies on the researcher’s subjective judgment, which can introduce bias into the sample selection process. This can lead to a sample that is not truly representative of the population, limiting the generalizability of the findings.

Question 60: Which of the following is an example of stratified sampling?

A. Selecting every 5th house on a street for a survey.

B. Dividing a company’s employees into departments and randomly selecting employees from each department in proportion to their representation in the company.

C. Randomly selecting 100 students from a school.

D. Choosing participants for a study based on their availability and willingness to participate.

Answer
Answer  : B. Stratified sampling involves dividing the population into homogeneous groups (strata) and then randomly selecting individuals from each stratum in a way that reflects their proportion in the overall population. Option B demonstrates this by dividing employees into departments and sampling proportionally from each.

CAIIB ABM Module A Unit 2 MCQs

 Biased Samples and their Impact – CAIIB ABM Module A Unit 2 MCQs

Question 61: What is a biased sample?

A. A sample that is too small.

B. A sample that is not representative of the population.

C. A sample that is selected randomly.

D. A sample that includes individuals from all segments of the population.

Answer
Answer  : B. A biased sample is one that does not accurately reflect the characteristics of the population from which it is drawn. This can lead to inaccurate or misleading conclusions about the population.

Question 62: Which of the following is NOT a potential source of bias in sampling?

A. Non-response bias

B. Selection bias

C. Random sampling error

D. Measurement error

Answer
Answer  : C. Random sampling error is the natural variation that occurs between different samples drawn from the same population. It is not a source of bias, but rather an inherent part of the sampling process. Non-response bias, selection bias, and measurement error are all potential sources of bias that can distort the results of a study.

Question 63: What is the impact of a biased sample on the results of a study?

A. It can lead to inaccurate or misleading conclusions about the population.

B. It has no impact on the results.

C. It improves the accuracy of the results.

D. It makes the results more generalizable to other populations.

Answer
Answer  : A. A biased sample can significantly compromise the validity and reliability of a study’s findings. If the sample is not representative of the population, any conclusions drawn from the sample data may not accurately reflect the true characteristics or opinions of the population.

Question 64: How can selection bias be minimized in sampling?

A. By using convenience sampling

B. By using random sampling methods

C. By relying on the researcher’s judgment

D. By excluding certain groups from the population

Answer
Answer  : B. Selection bias occurs when some members of the population have a higher or lower probability of being included in the sample than others. Random sampling methods, where every individual has an equal chance of being selected, are the most effective way to minimize selection bias.

Question 65: What is non-response bias?

A. Bias that occurs when some individuals in the sample refuse to participate or provide incomplete data.

B. Bias that occurs when the researcher selects individuals for the sample based on their own preferences.

C. Bias that occurs when the measurement instrument is faulty or inaccurate.

D. Bias that occurs when the sample size is too small.

Answer
Answer  : A. Non-response bias arises when individuals selected for the sample choose not to participate or provide incomplete information. This can lead to a biased sample if the non-respondents differ systematically from those who do participate.

CAIIB ABM Module A Unit 2 MCQs

Question 66: How can non-response bias be addressed in a study?

A. By ignoring the non-respondents and analyzing only the data from those who participated.

B. By using incentives or follow-up reminders to encourage participation.

C. By replacing non-respondents with individuals who are readily available.

D. By assuming that the non-respondents are similar to those who participated.

Answer
Answer  : B. To address non-response bias, researchers can employ strategies such as offering incentives, sending follow-up reminders, or using multiple modes of data collection to increase participation rates.

Question 67: Which of the following is an effective way to reduce the impact of bias in sampling?

A. Increase the sample size

B. Use convenience sampling

C. Rely on anecdotal evidence

D. Carefully design the sampling plan and use appropriate random sampling methods

Answer
Answer  : D. The most effective way to mitigate bias in sampling is to carefully design the sampling plan and employ appropriate random sampling techniques. This helps ensure that the sample is representative of the population and minimizes the potential for bias to distort the results.

Question 68: What is the potential consequence of ignoring bias in sampling?

A. The results of the study will be more accurate.

B. The study’s findings may not be generalizable to the population.

C. The sample size will automatically increase.

D. There will be no consequences.

Answer
Answer  : B. If bias is not addressed in sampling, the findings of the study may not accurately reflect the true characteristics or opinions of the population, limiting the ability to generalize the results beyond the sample.

Question 69: Which of the following is an example of measurement error?

A. Using a biased sampling method

B. Individuals refusing to participate in a survey

C. A poorly worded Question in a Questionnaire leading to inaccurate responses

D. Selecting a sample that is too small

Answer
Answer  : C. Measurement error refers to inaccuracies or inconsistencies in the data collection process, such as poorly designed survey Questions, faulty measurement instruments, or errors in recording or coding data.

Question 70: How can measurement error be minimized in a study?

A. By using a biased sampling method

B. By ignoring non-respondents

C. By using clear and unambiguous Questions in surveys and ensuring the accuracy of measurement instruments

D. By increasing the sample size

Answer
Answer  : C. To minimize measurement error, researchers should use clear and well-defined Questions in surveys, ensure the accuracy and reliability of measurement instruments, and train data collectors to avoid errors in recording or coding data.

CAIIB ABM Module A Unit 2 MCQs

Standard Error

Question 71: What does the standard error measure?

A. The accuracy of a single measurement

B. The variability of a population parameter

C. The variability of a sample statistic

D. The difference between the sample mean and the population mean

Answer
Answer  : C. The standard error quantifies the variability or spread of a sample statistic, such as the mean or proportion. It indicates how much we can expect the statistic to vary from sample to sample due to random sampling error.

Question 72: How is the standard error related to the sample size?

A. The standard error increases as the sample size increases.

B. The standard error decreases as the sample size increases.

C. The standard error is not affected by the sample size.

D. The relationship between standard error and sample size depends on the population distribution.

Answer
Answer  : B. The standard error is inversely proportional to the square root of the sample size. This means that as the sample size increases, the standard error decreases, indicating greater precision in estimating the population parameter.

Question 73: Which of the following statements is true about the standard error of the mean?

A. It is the standard deviation of the sampling distribution of the mean.

B. It measures the variability of individual observations in a sample.

C. It is always smaller than the population standard deviation.

D. It is calculated by dividing the population standard deviation by the sample size.

Answer
Answer  : A. The standard error of the mean is the standard deviation of the sampling distribution of the mean. It quantifies how much the sample means are expected to vary from the true population mean due to random sampling error.

Question 74: A researcher calculates the standard error of the mean for a sample and finds it to be 2.5. What does this indicate?

A. The sample mean is 2.5 units away from the population mean.

B. The sample standard deviation is 2.5.

C. On average, the sample means are expected to deviate from the population mean by about 2.5 units.

D. The sample size is 2.5.

Answer
Answer  : C. A standard error of the mean of 2.5 suggests that, on average, the means calculated from different samples drawn from the same population are likely to differ from the true population mean by approximately 2.5 units.

Question 75: How does a smaller standard error affect the precision of an estimate?

A. A smaller standard error indicates lower precision.

B. A smaller standard error indicates higher precision.

C. The standard error has no impact on precision.

D. The impact of the standard error on precision depends on the population distribution.

Answer
Answer  : B. A smaller standard error signifies less variability in the sample statistic, which translates to a more precise estimate of the corresponding population parameter.

CAIIB ABM Module A Unit 2 MCQs

 Statistical Inference – CAIIB ABM Module A Unit 2 MCQs

Question 76: What is statistical inference?

A. The process of collecting data from a population

B. The process of calculating descriptive statistics for a sample

C. The process of drawing conclusions about a population based on sample data

D. The process of designing experiments

Answer
Answer  : C. Statistical inference involves using sample data to make generalizations and draw conclusions about the larger population from which the sample was drawn. It relies on probability theory and sampling distributions to quantify the uncertainty associated with these inferences.

Question 77: Which of the following is NOT a component of statistical inference?

A. Estimation of population parameters

B. Hypothesis testing

C. Data visualization

D. Confidence intervals

Answer
Answer  : C. While data visualization can be a helpful tool in understanding and presenting data, it is not a core component of statistical inference. Estimation, hypothesis testing, and confidence intervals are all fundamental aspects of statistical inference.

Question 78: What is the purpose of hypothesis testing in statistical inference?

A. To prove that a theory is true

B. To collect data from a population

C. To make decisions about population parameters based on sample data

D. To calculate descriptive statistics

Answer
Answer  : C. Hypothesis testing is a statistical procedure used to make decisions about population parameters based on evidence from sample data. It involves formulating hypotheses, collecting data, and using statistical tests to assess the likelihood of the observed data under the assumption that the null hypothesis is true.

Question 79: What is a confidence interval?

A. A range of values that is guaranteed to contain the population parameter

B. A point estimate of a population parameter

C. A range of values that is likely to contain the population parameter with a certain level of confidence

D. The probability of rejecting the null hypothesis when it is true

Answer
Answer  : C. A confidence interval is a range of values calculated from sample data that is likely to include the true population parameter with a specified level of confidence. For example, a 95% confidence interval for the population mean indicates that if we were to repeat the sampling process many times, 95% of the calculated intervals would contain the true population mean.

Question 80: Which of the following is an example of statistical inference?

A. Calculating the average age of students in a classroom

B. Conducting a survey to collect data on customer satisfaction

C. Using a sample of voters to predict the outcome of an election

D. Creating a bar chart to display the distribution of income levels in a city

Answer
Answer  : C. Using a sample of voters to predict the outcome of an election is an example of statistical inference. It involves using sample data to make an inference or prediction about a larger population (in this case, all voters).

CAIIB ABM Module A Unit 2 MCQs

 Applications and Examples – CAIIB ABM Module A Unit 2 MCQs

Question 81: In which of the following fields is sampling NOT commonly used?

A. Market research

B. Quality control

C. Social sciences

D. Theoretical mathematics

Answer
Answer  : D. Sampling is widely used in various fields, including market research, quality control, and social sciences, to gather information about populations efficiently and cost-effectively. Theoretical mathematics, on the other hand, deals with abstract concepts and proofs and does not typically involve sampling from real-world populations.

Question 82: A company wants to estimate the average customer satisfaction rating for its new product. Which sampling method would be most appropriate?

A. Convenience sampling

B. Judgment sampling

C. Simple random sampling

D. Cluster sampling

Answer
Answer  : C. Simple random sampling is often a good choice for estimating population parameters like the average customer satisfaction rating. It ensures that every customer has an equal chance of being selected, reducing the potential for bias and allowing for generalizations to the entire customer base.

Question 83: A factory produces thousands of light bulbs every day. Which sampling method would be most efficient for quality control purposes?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : B. Systematic sampling, where every kth light bulb is selected for inspection, can be an efficient method for quality control in a factory setting. It is relatively easy to implement and ensures a spread of samples across the entire production run.

Question 84: A researcher wants to study the opinions of different age groups on a social issue. Which sampling method would be most appropriate?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : C. Stratified sampling is the most suitable method in this scenario. The researcher can divide the population into strata based on age groups (e.g., 18-25, 26-35, 36-45, etc.) and then randomly select individuals from each stratum to ensure representation from all age groups. This allows for comparisons of opinions across different age groups.

Question 85: A political campaign wants to gauge public opinion on a policy issue. Which sampling method would be most practical and cost-effective for reaching a large and diverse population?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : D. Cluster sampling can be a practical and cost-effective approach for surveying a large and diverse population. The population can be divided into clusters based on geographical regions or other relevant criteria, and then a random sample of clusters can be selected for further data collection. This reduces the logistical challenges and costs associated with reaching individuals spread across a wide area.

CAIIB ABM Module A Unit 2 MCQs

Question 86: A quality control inspector wants to check the weight of a batch of 1000 bags of flour. The bags are stacked on pallets, with each pallet holding 50 bags. Which sampling method would be most efficient for selecting bags to inspect?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : D. Cluster sampling would be efficient in this case. The pallets can be considered clusters, and a random sample of pallets can be selected. All bags on the selected pallets would then be inspected. This reduces the effort required to locate and access individual bags spread throughout the batch.

Question 87: A researcher is conducting a study on the effectiveness of a new teaching method in a school district. The district has several schools with varying student populations. Which sampling method would be most appropriate to ensure representation from all schools?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : C. Stratified sampling would be suitable here. The schools can be treated as strata, and students can be randomly selected from each school in proportion to the school’s student population. This ensures that all schools, regardless of their size, are represented in the sample.

Question 88: A company wants to conduct a survey to understand employee opinions on a new workplace policy. The company has a large number of employees spread across different departments and locations. Which sampling method would be most practical for gathering data efficiently?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : D. Cluster sampling can be a practical choice for surveying employees in a large company with multiple departments and locations. Departments or locations can be considered clusters, and a random sample of clusters can be selected. All employees within the chosen clusters would then participate in the survey.

Question 89: A researcher is studying the impact of a new drug on a specific medical condition. The researcher wants to ensure that the sample includes patients with varying degrees of severity of the condition. Which sampling method would be most appropriate?

A. Simple random sampling

B. Systematic sampling

C. Stratified sampling

D. Cluster sampling

Answer
Answer  : C. Stratified sampling would be beneficial in this scenario. Patients can be divided into strata based on the severity of their condition (e.g., mild, moderate, severe), and then a random sample of patients can be selected from each stratum. This ensures that the sample includes patients with different levels of disease severity, allowing for a more comprehensive assessment of the drug’s effectiveness.

Question 90: A market research firm wants to conduct a survey to understand consumer preferences for a new product. The firm has a limited budget and wants to collect data quickly. Which sampling method would be most suitable?

A. Convenience sampling

B. Judgment sampling

C. Simple random sampling

D. Cluster sampling

Answer
Answer  : A. Convenience sampling, where individuals are selected based on their easy availability and accessibility, might be the most suitable option in this scenario given the limited budget and time constraints. However, it’s important to acknowledge the potential for bias and limitations in generalizability with this method.

CAIIB ABM Module A Unit 2 MCQs

 Problem-solving exercises – CAIIB ABM Module A Unit 2 MCQs

Question 91: A population has a mean of 100 and a standard deviation of 15. If a random sample of 50 individuals is selected from this population, what is the standard error of the mean?

A. 15

B. 2.12

C. 3

D. 0.3

Answer
Answer  : B. The standard error of the mean is calculated by dividing the population standard deviation by the square root of the sample size: 

Standard Error = σ/√n = 15/√50 = 2.12

Question 92: A sample of 64 observations is drawn from a population with a mean of 50 and a standard deviation of 16. What is the probability that the sample mean will be less than 47?

A. 0.0668

B. 0.9332

C. 0.5

D. 0.4332

Answer
Answer  : A. 

1. Calculate the standard error: Standard Error = σ/√n = 16/√64 = 2

2. Calculate the z-score: z = (x – μ) / SE = (47 – 50) / 2 = -1.5

3. Find the probability: Using a standard normal distribution table or calculator, the probability of a z-score being less than -1.5 is 0.0668

Question 93: A population has a proportion of 0.60 who support a particular policy. If a random sample of 200 individuals is selected, what is the standard error of the proportion?

A. 0.0346

B. 0.6

C. 0.4

D. 120

Answer
Answer  : A. The standard error of the proportion is calculated as √[p(1-p)/n] where p is the population proportion and n is the sample size

Standard Error = √[(0.6 * 0.4)/200] 

= √0.0012

= 0.0346 

Question 94: The average weight of a certain breed of dog is 30 pounds with a standard deviation of 3 pounds. If a random sample of 36 dogs is selected, what is the probability that the sample mean weight will be between 29 and 31 pounds?

A. 0.6826

B. 0.9544

C. 0.3413

D. 0.1587

Answer
Answer  : B. 

1. Calculate the standard error: Standard Error = σ/√n = 3/√36 = 0.5

2. Calculate the z-scores: 

* For x = 29: z = (x – μ) / SE = (29 – 30) / 0.5 = -2

* For x = 31: z = (x – μ) / SE = (31 – 30) / 0.5 = 2

3. Find the probability: Using a standard normal distribution table or calculator, the probability of a z-score being between -2 and 2 is 0.9544 

Question 95: A machine produces parts with a mean diameter of 2 inches and a standard deviation of 0.05 inches. If a random sample of 100 parts is selected, what is the probability that the sample mean diameter will be greater than 2.01 inches?

A. 0.0228

B. 0.9772

C. 0.5

D. 0.4772

Answer
Answer  : A

1. Calculate the standard error: Standard Error = σ/√n = 0.05/√100 = 0.005

2. Calculate the z-score: z = (x – μ) / SE = (2.01 – 2) / 0.005 = 2

3. Find the probability: Using a standard normal distribution table or calculator, the probability of a z-score being greater than 2 is 0.0228

CAIIB ABM Module A Unit 2 MCQs

Question 96: A survey found that 75% of customers are satisfied with a company’s service. If a random sample of 400 customers is selected, what is the probability that the sample proportion of satisfied customers will be less than 70%?

A. 0.0104
B. 0.9938
C. 0.5
D. 0.4938

Answer
Answer: A.

Calculate the standard error of the proportion:
SE = √[p(1-p)/n] = √[(0.75 * 0.25)/400] = 0.02165

Calculate the z-score:
z = (p̂ – p) / SE = (0.70 – 0.75) / 0.02165 = -2.31

Find the probability: Using a standard normal distribution table or calculator, the probability of a z-score being less than -2.31 is approximately 0.0104.

Question 97: The mean lifespan of a certain type of battery is 50 hours with a standard deviation of 5 hours. If a random sample of 25 batteries is selected, what is the probability that the sample mean lifespan will be less than 48 hours?

A. 0.0228

B. 0.9772

C. 0.5

D. 0.4772

Answer
Answer  : A. 

1. Calculate the standard error: Standard Error = σ/√n = 5/√25 = 1

2. Calculate the z-score: z = (x – μ) / SE = (48 – 50) / 1 = -2

3. Find the probability: Using a standard normal distribution table or calculator, the probability of a z-score being less than -2 is 0.0228 

Question 98: The average monthly rent for a one-bedroom apartment in a city is $1200 with a standard deviation of $200. If a random sample of 100 apartments is selected, what is the probability that the sample mean rent will be more than $1250?

A. 0.0062

B. 0.9938

C. 0.5

D. 0.4938

Answer
Answer  : A. 

1. Calculate the standard error: Standard Error = σ/√n = 200/√100 = 20

2. Calculate the z-score: z = (x – μ) / SE = (1250 – 1200) / 20 = 2.5

3. Find the probability: Using a standard normal distribution table or calculator, the probability of a z-score being greater than 2.5 is 0.0062

Question 99: A population of 5000 students has an average GPA of 3.0 with a standard deviation of 0.4. If a random sample of 100 students is selected without replacement, what is the standard error of the mean?

A. 0.04

B. 0.0392

C. 3

D. 1.25

Answer
Answer  : B. 

Since we are sampling without replacement from a finite population, we need to use the finite population correction factor.

1. Calculate the standard error assuming an infinite population: SE = σ/√n = 0.4/√100 = 0.04

2. Calculate the finite population multiplier: fpc = √((N-n)/(N-1)) = √((5000-100)/(5000-1)) = 0.99

3. Calculate the corrected standard error: SE_corrected = SE * fpc = 0.04 * 0.99 = 0.0396. 

Rounded to two decimal places, the corrected standard error is 0.0392 

Question 100: A company produces a batch of 2000 products. A random sample of 50 products is selected, and 2 are found to be defective. What is the estimated proportion of defective products in the entire batch, and what is the standard error of this estimate?

A. Estimated proportion = 0.04, Standard Error = 0.0277

B. Estimated proportion = 0.04, Standard Error = 0.028

C. Estimated proportion = 0.4, Standard Error = 0.0196

D. Estimated proportion = 0.4, Standard Error = 0.028

Answer
Answer  : A. 

1. Calculate the estimated proportion: p̂ = Number of defective items in the sample / Sample size 

p̂ = 2 / 50 = 0.04

2. Calculate the standard error of the proportion: SE = √[p̂(1-p̂)/n] 

SE = √[(0.04 * 0.96)/50] 

SE = √0.000768

SE = 0.0277

Rounded to two decimal places, the standard error is 0.0277 

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