WorksheetsCAIIB_ABM_ch2
Total questions: 37
Worksheet time: 19mins
What does the term "population"
refer to in statistics?
Only humans in a given area.
Everything that is to be studied, not limited
to people.
A specific subset of a larger group.
The average value of a dataset.
What is a "sample" in statistics?
The same as a "population."
A portion or subset of the population.
The mean, median, mode, or standard
deviation of a dataset.
A characteristic of a parameter.
Which of the following is NOT a
characteristic of a population?
Mean
Median
Mode
Sample
When mean, median, mode, and
standard deviation describe the sample, what
are they called?
Parameters
Characteristics
Statistics
Variables
When mean, median, mode, and
standard deviation describe the population,
what are they called?
Parameters
Characteristics
Statistics
Variables
Which of the following is a characteristic of a
sample?
Parameter
Statistic
Mean
Median
What is a parameter in statistics?
A subset of the population
A characteristic of a sample
A characteristic of the population
A type of survey
What are strata in the context of statistics?
A statistical method for reducing risk and uncertainty.
Groups formed by dividing the population into
relatively homogeneous categories
The process of collecting data from the entire
population.
A measure of central tendency.
What is the purpose of sampling in
statistics?
To increase the risk and uncertainty in
decision-making.
To make data collection more time-
consuming and expensive.
To improve decision-making skills by
collecting data from a subset of the population
To ensure that the entire population is
tested.
Which of the following is an
example of why sampling is used?
Opening every packet of milk to test its
quality.
Contacting every individual who migrated
from one country to another.
Taking a bite of every sweet before buying
them.
Conducting a census every 10 years.
What is a census in the context of data
collection?
Collecting data from the entire population.
Dividing the population into strata.
Reducing the risk in decision-making.
A type of survey conducted every 10 years.
How many basic types of sampling are
there?
1
2
3
4
Which type of sampling involves using
personal knowledge or opinion to select items for the
sample?
Simple Random Sampling
Systematic Random Sampling
Stratified Sampling
Non-Random or Judgment Sampling
In judgment sampling, what is the basis
for selecting items for the sample?
Random chance
Statistical algorithms
Personal knowledge or opinion
Proximity to the researcher
Which of the following is an
example of non-random or judgment sampling?
Drawing names from a hat to select a sample.
Asking an experienced geologist to choose
exploration sites for an oil drilling company.
Dividing the population into homogeneous
groups.
Systematically selecting every 10th item from
a list.
Which type of sampling ensures that all
items in the population have a chance of being chosen
for the sample?
Non-Random or Judgment Sampling
Simple Random Sampling
Systematic Random Sampling
Stratified Sampling
In probability sampling, what is the key
characteristic?
Personal knowledge or opinion
Rigorous statistical analysis
Random chance for every item in the population
Homogeneous groups
What is a biased sample?
A sample that is selected randomly.
A sample that represents the entire population.
A sample that is influenced by personal opinions or
preferences.
A sample with a high level of statistical accuracy.
Why is rigorous statistical analysis more
challenging with judgment samples compared to
random probability samples?
Because judgment samples are easier to work
with.
Because random probability samples involve more
complex mathematics.
Because judgment samples are biased.
Because random probability samples lack
diversity.
What is the key characteristic of Simple
Random Sampling?
Selecting samples based on judgment and
personal opinions.
Allowing each possible sample to have an equal
probability of being chosen.
Dividing the population into strata.
Sampling with replacement.
How are elements selected in systematic
sampling?
Randomly
Based on personal judgment
At uniform intervals in time, order, or space
In a completely arbitrary manner
In systematic sampling, what is the key
characteristic of the selection process?
Random starting point
Uniform intervals
Equal chances for all items in the population
Non-uniform selection
In systematic sampling, how do you
select every 10th student on a college campus?
Choose the first 10 students in the directory.
Choose a random starting point and then select
every 10th name thereafter.
Randomly pick students from different locations.
Interview all students in alphabetical order.
How does systematic sampling differ
from simple random sampling?
In systematic sampling, every item in the entire
population has an equal chance of being selected.
In systematic sampling, each possible sample has
an equal chance of being selected.
In systematic sampling, elements are chosen
based on personal judgment.
In systematic sampling, the selection process is
random.
What is the first step in stratified
sampling?
Select elements randomly from the entire
population.
Divide the population into relatively
homogeneous groups.
Assign weights to each stratum.
Determine the total population size.
What is the purpose of stratifying the
population in stratified sampling?
To select elements randomly.
To create a biased sample.
To divide the population into groups based on
personal opinions.
To group similar elements together for more
effective sampling.
In stratified sampling, how are elements
selected from each stratum in the first approach?
Randomly, without considering the stratum
proportions.
Based on personal judgment.
In equal numbers from each stratum.
In the same ratio as the stratum to the whole
population.
In stratified sampling, what is the second
approach for selecting elements from each stratum?
Selecting elements with a uniform weight for
each stratum.
Randomly choosing elements without regard to
stratum proportions.
Assigning weights based on the total population.
Selecting an equal number of elements from
each stratum and adjusting the weight according to
the stratum proportion to the total population.
When is stratified sampling most
appropriate?
When the population is randomly distributed.
When the population consists of identical
elements.
When the population is already divided into
groups of different sizes.
When the population is too small to be divided.
In cluster sampling, what is the initial
step in the sampling process?
Selecting individual elements from the population.
Dividing the population into clusters.
Assigning weights to each cluster.
Determining the total population size.
What is the main assumption made in
cluster sampling?
Each cluster is identical to the others.
The population is homogenous.
The population is randomly distributed.
The selected clusters represent the population as a
whole.
What is the primary advantage of a
well-designed cluster sampling procedure over
simple random sampling?
It is more cost-effective.
It provides a more accurate representation of the
population.
It is faster to implement.
It works well for populations with small
variations.
In cluster sampling, how do you select
the sample?
By randomly selecting individual elements from the
population.
By choosing a random sample of the clusters.
By assigning weights to each cluster.
By using personal judgment to select clusters.
When is stratified sampling typically
used compared to cluster sampling?
When there is considerable variation within each
group.
When the population is divided into well-defined
groups.
When the groups are essentially similar to each
other.
When there is wide variation between the
groups.
What is a sampling distribution?
A representation of the population.
The process of dividing the population into strata.
The distribution of statistics (such as mean and
standard deviation) computed for different samples
drawn from the population.
A random selection of elements from the
population.
What happens to the mean and standard
deviation when you compute statistics for different
samples drawn from the same population?
They remain the same for all samples.
They become more consistent across samples.
They vary and are different for each sample.
They depend on the sample size but not on the
population.
In a sampling distribution, what is the
primary characteristic being studied?
The characteristics of the population.
The characteristics of the sample.
The variation in the statistics (e.g., mean and SD)
computed for different samples.
The total number of samples drawn.
