WGU C207 UPDATED ACTUAL QUESTIONS AND CORRECT ANSWERS
VERIFIED 2027 EDITION
1. Random Errors
Error in measurement caused by unpredictable statistical fluctuations
2. Information Bias
A prejudice in the data that results when either the respondent or the interviewer has an agenda and is
not presenting impartial questions or responding with truly honest responses, respectively
3. Ratio Data
Similar to interval data in that the data is ordered within a range and with each data point being an equal
interval apart, also has a natural zero point which indicates none of the given quality
4. Data Set
A collection of related data records on a storage device.
5. Nominal Data
Sometimes called categorical data or qualitative data, this data type is used to label subjects or data by
name
6. Reliable Data
Data that is consistent and repeatable
7. Davenport-Kim Three Stages Model
A decision-making model developed by Thomas Davenport and Jinho Kim that consists of three stages:
Framing the problem Solving the problem Communicating the problem
8. Blind study
A study performed where the participants are not told if they are in the treatment group or control group
9. Double-Blind Study
A study performed where neither the treatment allocators nor the participant knows which group the
participant is in
10. Measurement Bias
A prejudice in the data that results when the sample is not representative of the population being
represented.
11. Analytics
The discovery, analysis, and communication of meaningful patterns in data.
12. Data Management
The management, including the cleaning and storage, of collected data
13. Triple-Blind Study
A study performed where neither the treatment allocator nor the participant nor the response gatherer
knows which group the participant is in
14. Omission Error
An error because something ( for example, data or survey responses) is missing.
1
,15. Relational Database
A database structured to recognize relations among stored items of information
16. Ordinal Data
Data that places data objects into an order according o some quality with higher order indicating more of
that quality
17. Discrete Data
Data that can only take on whole values and has clear boundaries
18. Interval Data
Data that is ordered within a range and with each data point being an equal interval apart
19. Valid Data
Data resulting from a test that accurately measures what it is intended to measure
20. Big Data
A catch-phrase that describes a massive volume that is so large that it's difficult to process using
traditional database software techniques
21. Systematic errors
Errors in measurement that are constant within a data set, sometimes caused by faulty equipment or bias.
22. Benchmarks
Standards or points of reference for an industry or sector that can be used for comparison and evaluation.
23. Continuous Data
Data that can lay along any point in a range of data
24. Statistics
The science that deals with the interpretation of numerical facts or data though theories of probability.
Also, the numerical facts or data themselves.
25. Decision Tree Analysis
The diagram of possible alternatives and their expected consequences in order to formulate passible
courses of action in order to make decisions
26. Expected Monetary Value (EMV) Analysis
A statistical technique that calculates the average outcome when the future includes scenarios that may or
may not happen.
27. Response Rates
The number of people who respond to a marketing offer (often expressed as a percentage of the number
of responses divided by the number of people that ere targeted mail, email etc.)
28. Click-through rates
A common metric to measure the success of an email campaign. Total number of clicks on a hotlink(s) in
an email divided by the number of emails sent. Includes multiple clicks by a unique user.
29. Open Rate
The number of email message recipients who opened an email, usually as a percentage of the number of
emails sent or delivered. The open rate is considered a key metric for judging an email marketing
campaign.
2
, 30. Transactional Data
Data that records a time and relevant reference data needed for a particular transaction record; can be
financial, involving everything from a purchase to shipping status to a product return.
31. Response Bias
When respondents to a survey say what they believe the questioner wants to hear. This bias can occur as a
result of the wording of a question.
32. Conscious bias
The surveyor is actively seeking a certain response to support his or her cause. Bias occurs when the
researcher manipulates the phrasing of questions in order to elicit the desired response
33. Missing data and refusals
This misuse occurs when a certain part of the sample gets lost or subjects refuse to contribute ti the
overall data collection
34. Small sample size
Sample size is too small to make inferences
35. Parametric test
Assumes there is a structure (maybe a normal distribution) to the population, often appearing when mean
or standard deviation are important
36. Non-parametric test
Does not assume there to be a structure (maybe a normal distribution) to the population
37. Association and causality misuse
When a researcher notices a relationship between two variable and assumes that one variable is the cause
of the other. In reality, these variables might both be caused by separate variables; they would merely be
correlated. Or have no relationship at all.
38. Training and test data misuse
When the same dat that's used to forma hypothesis is then used to test the hypothesis. Sample size is too
small and different samples have a lot of crossover
39. Unfounded assumptions
Assumption is made that has not been proven
40. Operationalization
The development of specific research procedures that allow for observation and measurement of abstract
concepts. Key aspects of operationalizing is defining variables and attributes that adequately represent
the concepts of the study.
41. Blinding
When researchers place barriers between themselves and subjects to insure the researchers do not
influence subjects' behavior.
42. Causation
Cause and effect
43. Confidence Interval
The range around the mean that a sample has a specific probability of occurring within
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VERIFIED 2027 EDITION
1. Random Errors
Error in measurement caused by unpredictable statistical fluctuations
2. Information Bias
A prejudice in the data that results when either the respondent or the interviewer has an agenda and is
not presenting impartial questions or responding with truly honest responses, respectively
3. Ratio Data
Similar to interval data in that the data is ordered within a range and with each data point being an equal
interval apart, also has a natural zero point which indicates none of the given quality
4. Data Set
A collection of related data records on a storage device.
5. Nominal Data
Sometimes called categorical data or qualitative data, this data type is used to label subjects or data by
name
6. Reliable Data
Data that is consistent and repeatable
7. Davenport-Kim Three Stages Model
A decision-making model developed by Thomas Davenport and Jinho Kim that consists of three stages:
Framing the problem Solving the problem Communicating the problem
8. Blind study
A study performed where the participants are not told if they are in the treatment group or control group
9. Double-Blind Study
A study performed where neither the treatment allocators nor the participant knows which group the
participant is in
10. Measurement Bias
A prejudice in the data that results when the sample is not representative of the population being
represented.
11. Analytics
The discovery, analysis, and communication of meaningful patterns in data.
12. Data Management
The management, including the cleaning and storage, of collected data
13. Triple-Blind Study
A study performed where neither the treatment allocator nor the participant nor the response gatherer
knows which group the participant is in
14. Omission Error
An error because something ( for example, data or survey responses) is missing.
1
,15. Relational Database
A database structured to recognize relations among stored items of information
16. Ordinal Data
Data that places data objects into an order according o some quality with higher order indicating more of
that quality
17. Discrete Data
Data that can only take on whole values and has clear boundaries
18. Interval Data
Data that is ordered within a range and with each data point being an equal interval apart
19. Valid Data
Data resulting from a test that accurately measures what it is intended to measure
20. Big Data
A catch-phrase that describes a massive volume that is so large that it's difficult to process using
traditional database software techniques
21. Systematic errors
Errors in measurement that are constant within a data set, sometimes caused by faulty equipment or bias.
22. Benchmarks
Standards or points of reference for an industry or sector that can be used for comparison and evaluation.
23. Continuous Data
Data that can lay along any point in a range of data
24. Statistics
The science that deals with the interpretation of numerical facts or data though theories of probability.
Also, the numerical facts or data themselves.
25. Decision Tree Analysis
The diagram of possible alternatives and their expected consequences in order to formulate passible
courses of action in order to make decisions
26. Expected Monetary Value (EMV) Analysis
A statistical technique that calculates the average outcome when the future includes scenarios that may or
may not happen.
27. Response Rates
The number of people who respond to a marketing offer (often expressed as a percentage of the number
of responses divided by the number of people that ere targeted mail, email etc.)
28. Click-through rates
A common metric to measure the success of an email campaign. Total number of clicks on a hotlink(s) in
an email divided by the number of emails sent. Includes multiple clicks by a unique user.
29. Open Rate
The number of email message recipients who opened an email, usually as a percentage of the number of
emails sent or delivered. The open rate is considered a key metric for judging an email marketing
campaign.
2
, 30. Transactional Data
Data that records a time and relevant reference data needed for a particular transaction record; can be
financial, involving everything from a purchase to shipping status to a product return.
31. Response Bias
When respondents to a survey say what they believe the questioner wants to hear. This bias can occur as a
result of the wording of a question.
32. Conscious bias
The surveyor is actively seeking a certain response to support his or her cause. Bias occurs when the
researcher manipulates the phrasing of questions in order to elicit the desired response
33. Missing data and refusals
This misuse occurs when a certain part of the sample gets lost or subjects refuse to contribute ti the
overall data collection
34. Small sample size
Sample size is too small to make inferences
35. Parametric test
Assumes there is a structure (maybe a normal distribution) to the population, often appearing when mean
or standard deviation are important
36. Non-parametric test
Does not assume there to be a structure (maybe a normal distribution) to the population
37. Association and causality misuse
When a researcher notices a relationship between two variable and assumes that one variable is the cause
of the other. In reality, these variables might both be caused by separate variables; they would merely be
correlated. Or have no relationship at all.
38. Training and test data misuse
When the same dat that's used to forma hypothesis is then used to test the hypothesis. Sample size is too
small and different samples have a lot of crossover
39. Unfounded assumptions
Assumption is made that has not been proven
40. Operationalization
The development of specific research procedures that allow for observation and measurement of abstract
concepts. Key aspects of operationalizing is defining variables and attributes that adequately represent
the concepts of the study.
41. Blinding
When researchers place barriers between themselves and subjects to insure the researchers do not
influence subjects' behavior.
42. Causation
Cause and effect
43. Confidence Interval
The range around the mean that a sample has a specific probability of occurring within
3
