1. What is 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.
2. How to reduce sampling error
Increase the sample size or use stratified random sampling.
3. What are non-sampling errors? Give an example.
Errors that are not the result of random sampling. E.g. measurement bias,
response bias, selection bias.
4. What is measurement bias? Give an example
May result from a mistake during measurement process or poorly worded
questions. E.g. Scale on carpet overestimates weight.
5. What is a response bias? Give an example
When participants respond in an untruthful or inaccurate way. E.g. Survey
about being incarcerated in the past.
6. What is selection bias? Give an example
When the sample is not representative of the population. E.g. Literary Digest
predicted Landon as the next president but the sample was upper class
people who tend to vote republican.
7. What is the difference between a statistic and a parameter?
A statistic is a descriptive statistical result that is generated from a sample,
whereas a parameter is a statistical result from a population.
8. A sample is composed of members that generally possess the same
characteristics as those of the population. They allow for accurate inferences
to be made.
Representative sample.
9. A sample is not representative of the population. It is attributed to the
researcher and data collection methods, and do not allow for accurate
inferences to be made.
Biased
10. This method of sampling is when every member of the population has an
equal chance of being selected, this increases the reliability of results. Each
member is independent of the others: selecting one member does not
increase or decrease the likelihood of another member being selected.
Random sampling
11. This method of sampling is when each individual in the population of size
has an equal chance of being selected for the sample, and relies on chance
to create a representative sample. This is NOT feasible for large populations.
Simple random sampling
12. This method of sampling is a type of random sampling when populations
can be subdivided into groups called strata. It randomly gathers data from
subgroups within a sample. It is used when researchers wants to compare
outcomes for different subgroups within a population or to compare
outcomes within subgroups
Stratified sampling
, 13. Two types of stratified sampling:
Proportional, non-proportional.
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.
2. How to reduce sampling error
Increase the sample size or use stratified random sampling.
3. What are non-sampling errors? Give an example.
Errors that are not the result of random sampling. E.g. measurement bias,
response bias, selection bias.
4. What is measurement bias? Give an example
May result from a mistake during measurement process or poorly worded
questions. E.g. Scale on carpet overestimates weight.
5. What is a response bias? Give an example
When participants respond in an untruthful or inaccurate way. E.g. Survey
about being incarcerated in the past.
6. What is selection bias? Give an example
When the sample is not representative of the population. E.g. Literary Digest
predicted Landon as the next president but the sample was upper class
people who tend to vote republican.
7. What is the difference between a statistic and a parameter?
A statistic is a descriptive statistical result that is generated from a sample,
whereas a parameter is a statistical result from a population.
8. A sample is composed of members that generally possess the same
characteristics as those of the population. They allow for accurate inferences
to be made.
Representative sample.
9. A sample is not representative of the population. It is attributed to the
researcher and data collection methods, and do not allow for accurate
inferences to be made.
Biased
10. This method of sampling is when every member of the population has an
equal chance of being selected, this increases the reliability of results. Each
member is independent of the others: selecting one member does not
increase or decrease the likelihood of another member being selected.
Random sampling
11. This method of sampling is when each individual in the population of size
has an equal chance of being selected for the sample, and relies on chance
to create a representative sample. This is NOT feasible for large populations.
Simple random sampling
12. This method of sampling is a type of random sampling when populations
can be subdivided into groups called strata. It randomly gathers data from
subgroups within a sample. It is used when researchers wants to compare
outcomes for different subgroups within a population or to compare
outcomes within subgroups
Stratified sampling
, 13. Two types of stratified sampling:
Proportional, non-proportional.
