Portage Statistics CNSL 503 Module 3 STUDY
GUIDE 2025/2026 QUESTIONS BANK AND VERIFIED
CORRECT SOLUTIONS || 100% GUARANTEED PASS
<RECENT VERSION>
1. What is the difference between a parameter and a statistic? - ANSWER ✓ A
parameter is a numerical value that describes a population, such as the
population mean. A statistic is a numerical value that describes a sample,
such as a sample mean
A statistic is a descriptive statistical result that is generated from a sample,
whereas a parameter is a statistical result from a population..
2. Why is it important to have a representative sample? - ANSWER ✓ It is
important to have a representative sample, because we're trying to make
inferences about a certain population. In order to make correct inferences
about this population, the sample must reflect the population. For instance,
not having a representative sample is what called Literary Digest to predict
Alf Landon to win, when FDR actually won in a land slide.
3. Representative sample - ANSWER ✓ consists of members that possess the
same characteristics as those of the population
(e.g. age distribution)
4. biased sample - ANSWER ✓ a sample that is not representative of the
population
5. simple random sampling - ANSWER ✓ -every member of the population
has an equal chance of being selected for the sample
-the probability that each member is selected is independent of one another
-computers are often used to generate random numbers
-best for small sample sizes
,6. systematic sampling - ANSWER ✓ random sampling with a system in
which the starting point is random and each subsequent member selected is
based on a fixed interval (e.g. every 3rd person)
- the probability that each member is selected is not independent of one
another
-random samples can be achieved in a way that is more efficient than simple
random samlping
7. stratified sampling - ANSWER ✓ is when you divide the population into
strata (typically based on age, gender, race, socioeconomic status) and then
you randomly select people to sample from each strata
(not everyone in a strata is studied)
-great for populations with subsections/ different characterstics
8. cluster sampling - ANSWER ✓ is when you divide the population into
clusters such as the U.S. voters into voters by states, and then you randomly
select clusters (e.g. CA, NY, KY). From there, everyone in that cluster is
studied.
-great for very large populations
9. convenience sampling - ANSWER ✓ selecting a sample that is convenient
and easy to access
- asking everyone in a lecture hall to determine L handedness at your
school
-does NOT result in a representative sample
-generally is more prone to bias than other sampling methods
10.Wha is sampling error ? - ANSWER ✓ Sampling error is the difference
between the population parameter and the sample statistic. It is impossible to
eliminate sampling error but there are ways to reduce it such as a larger
sample size.
11.How to reduce sampling error? - ANSWER ✓ Increase the sample size or
use STRATIFIED sampling (increase the likelihood that the sample is more
representative of the population)
, - the larger the sampling size the smaller the sampling error
12.What are non-sampling errors? aka as?
Examples? - ANSWER ✓ errors that are not the result of random sampling
-sampling bias
e.g. measurement bias, response bias, selection bias
13.measurement bias - ANSWER ✓ may results from a mistake during the
measurement process or poorly worded questions
e.g. scale on carpet overestimates weight
14.response bias - ANSWER ✓ when participants respond in a way that is
inaccurate or untruthful
15.selection bias - ANSWER ✓ when the sample is not representative of the
population
16.( e.g. Literary Digest Alf Landon predicted to vote but the sample was upper
class people who tend to vote republican)
17.What does it mean if the sample mean falls in the critical region? -
ANSWER ✓ it is sufficiently unlikely to be the same as the untreated
population (reject the null)
18.The sample statistic is converted into a ____ - ANSWER ✓ test statistic, to
be compared against the critical value
19.If the mean of sample falls within the critical region... - ANSWER ✓ reject
the null
20.If the mean of sample falls outside the critical region... - ANSWER ✓ fail to
reject the null
21.Type I error - ANSWER ✓ decision was to reject the null, but the null was
actually tru and the treatment doesn't work
GUIDE 2025/2026 QUESTIONS BANK AND VERIFIED
CORRECT SOLUTIONS || 100% GUARANTEED PASS
<RECENT VERSION>
1. What is the difference between a parameter and a statistic? - ANSWER ✓ A
parameter is a numerical value that describes a population, such as the
population mean. A statistic is a numerical value that describes a sample,
such as a sample mean
A statistic is a descriptive statistical result that is generated from a sample,
whereas a parameter is a statistical result from a population..
2. Why is it important to have a representative sample? - ANSWER ✓ It is
important to have a representative sample, because we're trying to make
inferences about a certain population. In order to make correct inferences
about this population, the sample must reflect the population. For instance,
not having a representative sample is what called Literary Digest to predict
Alf Landon to win, when FDR actually won in a land slide.
3. Representative sample - ANSWER ✓ consists of members that possess the
same characteristics as those of the population
(e.g. age distribution)
4. biased sample - ANSWER ✓ a sample that is not representative of the
population
5. simple random sampling - ANSWER ✓ -every member of the population
has an equal chance of being selected for the sample
-the probability that each member is selected is independent of one another
-computers are often used to generate random numbers
-best for small sample sizes
,6. systematic sampling - ANSWER ✓ random sampling with a system in
which the starting point is random and each subsequent member selected is
based on a fixed interval (e.g. every 3rd person)
- the probability that each member is selected is not independent of one
another
-random samples can be achieved in a way that is more efficient than simple
random samlping
7. stratified sampling - ANSWER ✓ is when you divide the population into
strata (typically based on age, gender, race, socioeconomic status) and then
you randomly select people to sample from each strata
(not everyone in a strata is studied)
-great for populations with subsections/ different characterstics
8. cluster sampling - ANSWER ✓ is when you divide the population into
clusters such as the U.S. voters into voters by states, and then you randomly
select clusters (e.g. CA, NY, KY). From there, everyone in that cluster is
studied.
-great for very large populations
9. convenience sampling - ANSWER ✓ selecting a sample that is convenient
and easy to access
- asking everyone in a lecture hall to determine L handedness at your
school
-does NOT result in a representative sample
-generally is more prone to bias than other sampling methods
10.Wha is sampling error ? - ANSWER ✓ Sampling error is the difference
between the population parameter and the sample statistic. It is impossible to
eliminate sampling error but there are ways to reduce it such as a larger
sample size.
11.How to reduce sampling error? - ANSWER ✓ Increase the sample size or
use STRATIFIED sampling (increase the likelihood that the sample is more
representative of the population)
, - the larger the sampling size the smaller the sampling error
12.What are non-sampling errors? aka as?
Examples? - ANSWER ✓ errors that are not the result of random sampling
-sampling bias
e.g. measurement bias, response bias, selection bias
13.measurement bias - ANSWER ✓ may results from a mistake during the
measurement process or poorly worded questions
e.g. scale on carpet overestimates weight
14.response bias - ANSWER ✓ when participants respond in a way that is
inaccurate or untruthful
15.selection bias - ANSWER ✓ when the sample is not representative of the
population
16.( e.g. Literary Digest Alf Landon predicted to vote but the sample was upper
class people who tend to vote republican)
17.What does it mean if the sample mean falls in the critical region? -
ANSWER ✓ it is sufficiently unlikely to be the same as the untreated
population (reject the null)
18.The sample statistic is converted into a ____ - ANSWER ✓ test statistic, to
be compared against the critical value
19.If the mean of sample falls within the critical region... - ANSWER ✓ reject
the null
20.If the mean of sample falls outside the critical region... - ANSWER ✓ fail to
reject the null
21.Type I error - ANSWER ✓ decision was to reject the null, but the null was
actually tru and the treatment doesn't work
