Test: Sampling Theory - 1 - SSC CGL MCQ

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

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

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

Both (a) and (b).

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

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

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

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

Sampling errors

Non-sampling errors

Either (a) or (b)

Both (a) and (b)

A finite population

An infinite population

A hypothetical population

An imaginary population.

Sample observations

A sampling frame

Sample survey

Complete enumeration

Haphazard sampling

Probability sampling

Systematic sampling

Sampling with the same probability for each unit

Population

Sample

Both (a) and (b)

(a) or (b)

A function of sample observations

A function of population units

A characteristic of a population

A part of a population

The variation in the values of a statistic

The variation in the values of a sample

The differences in the values of a parameter

The variation in the values of observations

The distribution of sample observations

The distribution of random samples

The distribution of a parameter

The probability distribution of a statistic

The error committed in sampling

The error committed in sample survey

The error committed in estimating a parameter

Standard deviation of a statistic

Standard error

Sampling fluctuation

Sampling distribution

Expectation

Increases

Decreases

Remains constant

Decreases proportionately

300

625

50

600

100

15

125

25

The population is not very large

The population is not much heterogeneous

The population is partitioned into several sections.

Both (a) and (b)

A probabilistic sampling

A non- probabilistic sampling

A mixed sampling

Both (b) and (c)

Sample size is proportional to the population size

Sample size is proportional to the sample SD

Sample size is proportional to the sample variance

Population size is proportional to the sample variance

Multistage sampling

Stratified sampling

Simple random sampling

Systematic sampling

Simple random sampling

Multistage sampling

Stratified sampling

Systematic sampling

Simple random sampling

Stratified sampling

Multistage sampling

Systematic sampling

Systematic sampling

Simple random sampling

Purposive sampling

Quota sampling

Unbiasedness, consistency, efficiency and sufficiency

Unbiasedness, expectation, sampling and estimation

Estimation, consistency, sufficiency and efficiency

Estimation, expectation, unbiasedness and sufficiency

A biased estimator  

An unbiased estimator 

A biased estimator for population SD

A biased estimator for population variance

An MVUE for population mean

A consistent and efficient estimator for population mean

A sufficient estimator for population mean

All of these

Only one

Two

Three

Many

95 percent

90 percent

94 percent

98 percent

(3,6),(3,1),(6,1)

(3,3),(6,6),(1,1)

(3,3),(3,6),(3,1),(6,6),(6,3),(6,1),(1,1),(1,3),(1,6)

(1,1),(1,3),(1,6),(6,1),(6,2),(6,3),(6,6),(1,6),(1,1)

(a, b),(a, c),(a, d)

(a, b),(b, c), (c, d)

(a, b), (b, a), (a, c),(c,a), (a, d), (d, a)

(a, b), (a, c), (a, d), (b, c), (b, d), (c,d)

0.01

0.05

0.028

0.0593