Sampling Theory In Statistics: Quiz! Test

42 Questions | Total Attempts: 641

SettingsSettingsSettings
Sampling Theory In Statistics: Quiz! Test - Quiz

.


Questions and Answers
  • 1. 
    Sampling can be described as a statistical procedure:
    • A. 

      To infer about the unknown universe from a knowledge of any sample

    • B. 

      To infer about the known universe from a knowledge of a sample drawn from it

    • C. 

      To infer about the unknown universe from a knowledge of a random sample drawn from it

    • D. 

      Both (a) and (b)

  • 2. 
    The Law of Statistical Regularity says that:
    • A. 

      Sample drawn from the population under discussion possesses the characteristics of the population

    • B. 

      A large sample drawn at random from the population would posses the characteristics of the population

    • C. 

      A large sample drawn at random from the population would possess the characteristics of the population on an average

    • D. 

      An optimum level of efficiency can be attained at a minimum cost

  • 3. 
    A sample survey is prone to:
    • A. 

      Sampling errors

    • B. 

      Non-sampling errors

    • C. 

      Either (a) or (b)

    • D. 

      Both (a) and (b)

  • 4. 
    The population of roses in Salt Lake City is an example of:
    • A. 

      A finite population

    • B. 

      An infinite population

    • C. 

      A hypothetical population

    • D. 

      An imaginary population

  • 5. 
    Statistical decision about an unknown universe is taken on the basis of:
    • A. 

      Sample observations

    • B. 

      A sampling frame

    • C. 

      Sample survey

    • D. 

      Complete enumeration

  • 6. 
    Random sampling implies:
    • A. 

      Haphazard sampling

    • B. 

      Probability sampling

    • C. 

      Systematic sampling

    • D. 

      Sampling with the same probability for each unit

  • 7. 
    A parameter is a characteristic of:
    • A. 

      Population

    • B. 

      Sample

    • C. 

      Both (a) and (b)

    • D. 

      (a) or (b)

  • 8. 
    A statistic is:
    • A. 

      A function of sample observations

    • B. 

      A function of population units

    • C. 

      A characteristic of a population

    • D. 

      A part of a population

  • 9. 
    Sampling Fluctuations may be described as:
    • A. 

      The variation in the values of a statistic

    • B. 

      The variation in the values of a sample

    • C. 

      The differences in the values of a parameter

    • D. 

      The variation in the values of observations

  • 10. 
    The sampling distribution is:
    • A. 

      The distribution of sample observations

    • B. 

      The distribution of random samples

    • C. 

      The distribution of a parameter

    • D. 

      The probability distribution of a statistic

  • 11. 
    Standard error can be described as:
    • A. 

      The error committed in sampling

    • B. 

      The error committed in sample survey

    • C. 

      The error committed in estimating a parameter

    • D. 

      Standard deviation of a statistic

  • 12. 
    A measure of precision obtained by sampling is given by:
    • A. 

      Standard error

    • B. 

      Sampling fluctuation

    • C. 

      Sampling distribution

    • D. 

      Expectation

  • 13. 
    As the sample size increases, standard error:
    • A. 

      Increases

    • B. 

      Decreases

    • C. 

      Remains constant

    • D. 

      Decrease proportionately

  • 14. 
    If from a population with 25 members, a random sample without replacement of 2 members is taken, the number of all such samples is:
    • A. 

      300

    • B. 

      625

    • C. 

      50

    • D. 

      600

  • 15. 
    A population comprises 5 members. The number of all possible samples of size 2 that can be drawn from it with replacement is:
    • A. 

      100

    • B. 

      15

    • C. 

      125

    • D. 

      25

  • 16. 
    Simple random sampling is very effective if:  
    • A. 

      The population is not very large

    • B. 

      The population is not much heterogeneous

    • C. 

      The population is partitioned into several sections

    • D. 

      Both (a) and (b)

  • 17. 
    Simple random sampling is:  
    • A. 

      A probabilistic sampling

    • B. 

      A non-probabilistic sampling

    • C. 

      A mixed sampling

    • D. 

      Both (b) and (c)

  • 18. 
    According to Neyman's allocation, in stratified sampling:
    • A. 

      Sample size is proportional to the population size

    • B. 

      Sample size is proportional to the sample SD

    • C. 

      Sample size is proportional to the sample variance

    • D. 

      Population size is proportional to the sample variance

  • 19. 
    Which sampling provides separate estimates for population means for different segments and also an overall estimate?
    • A. 

      Multistage sampling

    • B. 

      Stratified sampling

    • C. 

      Simple random sampling

    • D. 

      Systematic sampling

  • 20. 
    Which sampling adds flexibility to the sampling process?
    • A. 

      Simple random sampling

    • B. 

      Multistage sampling

    • C. 

      Stratified sampling

    • D. 

      Systematic sampling

  • 21. 
    Which sampling is affected most if the sampling frame contains an undetected periodicity?                                              
    • A. 

      Simple random sampling

    • B. 

      Stratified sampling

    • C. 

      Multistage sampling

    • D. 

      Systematic sampling

  • 22. 
    Which sampling is subjected to the discretion of the sampler?  
    • A. 

      Systematic sampling

    • B. 

      Simple random sampling

    • C. 

      Purposive sampling

    • D. 

      Quota sampling

  • 23. 
    The criteria for an ideal estimator are:  
    • A. 

      Unbiasedness, consistency, efficiency and sufficiency

    • B. 

      Unbiasedness, expectation, sampling and estimation

    • C. 

      Estimation, consistency, sufficiency and efficiency

    • D. 

      Estimation, expectation, unbiasedness and sufficiency

  • 24. 
    The sample standard deviation is:  
    • A. 

      A biased estimator

    • B. 

      An unbiased estimator

    • C. 

      A biased estimator for population SD

    • D. 

      A biased estimator for population variance

  • 25. 
    The sample mean is:
    • A. 

      An MVUE for population mean

    • B. 

      A consistent and efficient estimator for population mean

    • C. 

      A sufficient estimator for population mean

    • D. 

      All of these

Back to Top Back to top