CNSL 503: A GUIDE TO A+. QUESTIONS AND ANSWERS
FORMAT.LATEST UPDATE
What is the difference between a parameter and a statistic?
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..
Why is it important to have a representative sample?
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.
Representative sample
consists of members that possess the same characteristics as those of the population
(e.g. age distribution)
biased sample
a sample that is not representative of the population
simple random sampling
-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
systematic sampling
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
stratified sampling
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
, cluster sampling
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
convenience sampling
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
Wha is sampling error ?
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.
How to reduce sampling error?
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
What are non-sampling errors? aka as?
Examples?
errors that are not the result of random sampling
-sampling bias
e.g. measurement bias, response bias, selection bias
measurement bias
may results from a mistake during the measurement process or poorly worded questions
e.g. scale on carpet overestimates weight
response bias
when participants respond in a way that is inaccurate or untruthful
selection bias
when the sample is not representative of the population
( e.g. Literary Digest Alf Landon predicted to vote but the sample was upper class people who tend to
vote republican)
FORMAT.LATEST UPDATE
What is the difference between a parameter and a statistic?
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..
Why is it important to have a representative sample?
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.
Representative sample
consists of members that possess the same characteristics as those of the population
(e.g. age distribution)
biased sample
a sample that is not representative of the population
simple random sampling
-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
systematic sampling
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
stratified sampling
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
, cluster sampling
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
convenience sampling
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
Wha is sampling error ?
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.
How to reduce sampling error?
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
What are non-sampling errors? aka as?
Examples?
errors that are not the result of random sampling
-sampling bias
e.g. measurement bias, response bias, selection bias
measurement bias
may results from a mistake during the measurement process or poorly worded questions
e.g. scale on carpet overestimates weight
response bias
when participants respond in a way that is inaccurate or untruthful
selection bias
when the sample is not representative of the population
( e.g. Literary Digest Alf Landon predicted to vote but the sample was upper class people who tend to
vote republican)
