STAT 121 Commonly Tested Questions Verified Answers
What is statistics?
-Science of extracting meaning from data
-Art of persuading the universe to divulge information about itself
-Methodology for using data to answer questions in the presence of variation
What is the dogma of statistics?
There is always variation, which leads to uncertainty
What is the big picture in statistics?
Population-->1.Produce Data-->data (2. exploratory data analysis)-->3. Probability-->4.
Inference
What is a population?
ENTIRE GROUP of individuals you want to study. GENERALLY TOO BIG TO ACTUALLY
MEASURE/OBSERVE.
What is a sample?
Subgroup of the population that we can actually measure, observe, and collect data from.
What is part of step 1. Produce Data, from the big picture of statistics?
Choosing your sample
Define quantitative variable
type of variable whose possible values are meaningful numbers Ex-weight
Define Categorical Variables
Possible responses are non-quantitative (words, labels, attributes). Example-Zip Code, sex,
mortality, race, social class
What is the goal of statistics?
,Convert data into useful information
Define data set
Data identified with contextual information (who was observed, what was measured, why is the
study being done), usually given in a table
Single Variable Pattern
: distribution of a variable = summary of data one variable at a time
- what values does the variable take?
- how often do these values occur?
Why sample compared to a census?
Samples are:
-Practical
-Cheap
-Often more accurate
Parameter
Number that describes a Population
Statistic
Number with a Sample
What can lack of randomization lead to?
-Biased results
-Sample not being representative of the population
-Laws of probability cannot be used to measure chance variation/perform inference
What are the ways to sample badly (non-probability sampling)?
-Convenience Sampling (first respondents you can find)
-Volunteer Response Sampling (individuals self-select like in tv polls)
,What's wrong with non-probability samples?
-Bias; the sample favors certain outcomes and not representative of the population
-Impossible to assess uncertainty
What are the good samples for probability sampling design and what makes them good?
-They all use some random device to select
-Representative of the population
-Uncertainty can be assessed
-Ex: Simple Random Sampling (SRS), Cluster, Stratified Random Sampling, and multistage
sampling.
Define Simple Random Sampling
(SRS) Taking SOME individuals from the population. Equivalent of selecting names out of a hat.
Every possible option has an equal chance of being selected
Define Cluster Sampling
ALL individuals from SOME groups. A random sample of clusters (groups that population are
naturally divided into) is taken and all the individuals in the selected clusters are included in the
sample.
Define Stratified Random Sampling
SOME from ALL (or EVERY) group. Select a SRS (simple random sample) from each stratum
(groups that share a similar characteristic, ex; gender), then combine all together
Multistage Sampling
SOME from SOME groups. Takes sample at each hierarchical level of the population.
Define Non-Sampling Bias and give examples
-Other sources of error (not measurable, so must be eliminated or minimized)
Undercoverage
Non-Response
, Misleading
Interviewer
Question Order
Open and Closed Questions
Question Wording
Define Undercoverage
no possibility to be selected for the sample. Ex-homeless or no phone
Define Non-Response
Individuals choose not to be in the sample because they refuse to provide information or
cannot be contacted Ex-Too long of a survey and so some refuse to complete it
Define Misleading Response
People lie/inaccurate answers (sensitive information)
Interviewer Bias
Person asking questions influences responses
Question Order Bias
The order that questions are asked promotes certain responses. Ex-debt before levels of
happiness or happiness before debt question
Open Questions & Closed Questions in Sample Surveys
Open: unlimited responses, short answer, more difficult to analyze
Closed; limit responses, MC, easier to analyze, should include other/unsure
Question Wording
The way a question is asked may lead on to an answer, mislead, or confuse
Observational Studies
What is statistics?
-Science of extracting meaning from data
-Art of persuading the universe to divulge information about itself
-Methodology for using data to answer questions in the presence of variation
What is the dogma of statistics?
There is always variation, which leads to uncertainty
What is the big picture in statistics?
Population-->1.Produce Data-->data (2. exploratory data analysis)-->3. Probability-->4.
Inference
What is a population?
ENTIRE GROUP of individuals you want to study. GENERALLY TOO BIG TO ACTUALLY
MEASURE/OBSERVE.
What is a sample?
Subgroup of the population that we can actually measure, observe, and collect data from.
What is part of step 1. Produce Data, from the big picture of statistics?
Choosing your sample
Define quantitative variable
type of variable whose possible values are meaningful numbers Ex-weight
Define Categorical Variables
Possible responses are non-quantitative (words, labels, attributes). Example-Zip Code, sex,
mortality, race, social class
What is the goal of statistics?
,Convert data into useful information
Define data set
Data identified with contextual information (who was observed, what was measured, why is the
study being done), usually given in a table
Single Variable Pattern
: distribution of a variable = summary of data one variable at a time
- what values does the variable take?
- how often do these values occur?
Why sample compared to a census?
Samples are:
-Practical
-Cheap
-Often more accurate
Parameter
Number that describes a Population
Statistic
Number with a Sample
What can lack of randomization lead to?
-Biased results
-Sample not being representative of the population
-Laws of probability cannot be used to measure chance variation/perform inference
What are the ways to sample badly (non-probability sampling)?
-Convenience Sampling (first respondents you can find)
-Volunteer Response Sampling (individuals self-select like in tv polls)
,What's wrong with non-probability samples?
-Bias; the sample favors certain outcomes and not representative of the population
-Impossible to assess uncertainty
What are the good samples for probability sampling design and what makes them good?
-They all use some random device to select
-Representative of the population
-Uncertainty can be assessed
-Ex: Simple Random Sampling (SRS), Cluster, Stratified Random Sampling, and multistage
sampling.
Define Simple Random Sampling
(SRS) Taking SOME individuals from the population. Equivalent of selecting names out of a hat.
Every possible option has an equal chance of being selected
Define Cluster Sampling
ALL individuals from SOME groups. A random sample of clusters (groups that population are
naturally divided into) is taken and all the individuals in the selected clusters are included in the
sample.
Define Stratified Random Sampling
SOME from ALL (or EVERY) group. Select a SRS (simple random sample) from each stratum
(groups that share a similar characteristic, ex; gender), then combine all together
Multistage Sampling
SOME from SOME groups. Takes sample at each hierarchical level of the population.
Define Non-Sampling Bias and give examples
-Other sources of error (not measurable, so must be eliminated or minimized)
Undercoverage
Non-Response
, Misleading
Interviewer
Question Order
Open and Closed Questions
Question Wording
Define Undercoverage
no possibility to be selected for the sample. Ex-homeless or no phone
Define Non-Response
Individuals choose not to be in the sample because they refuse to provide information or
cannot be contacted Ex-Too long of a survey and so some refuse to complete it
Define Misleading Response
People lie/inaccurate answers (sensitive information)
Interviewer Bias
Person asking questions influences responses
Question Order Bias
The order that questions are asked promotes certain responses. Ex-debt before levels of
happiness or happiness before debt question
Open Questions & Closed Questions in Sample Surveys
Open: unlimited responses, short answer, more difficult to analyze
Closed; limit responses, MC, easier to analyze, should include other/unsure
Question Wording
The way a question is asked may lead on to an answer, mislead, or confuse
Observational Studies
