WGU C207 Data-Driven Decision Making OA
Exam – 100 Practice Questions with Detailed
Verified Answers and Rationales – Pass on First
Attempt – 2026 Updated
INTRO:
*This comprehensive study guide is designed for students preparing for the WGU C207
Data-Driven Decision Making Objective Assessment. The exam evaluates understanding of
quantitative analysis, statistical tools, quality metrics, and data-driven decision-making in
business contexts. Competencies include: The Case for Quantitative Analysis, Statistics as
a Managerial Tool, Quantitative Statistical Tools, Quality Metrics and Tools, Real World
Data-Driven Decisions, and Improving Organizational Performance . This resource includes
definition-based questions with detailed rationales. Answers are in bold italic * with
explanations in italic at the end of each question.
CORE DOMAINS COVERED
Domain Key Topics
The Case for Davenport-Kim three-stage model (Framing, Solving,
Quantitative Communicating), research validity, best practices vs. ethics,
Analysis sampling (size and randomness), bias types (measurement,
conscious, information), reliability vs. validity
Statistics as a Descriptive, Predictive, and Prescriptive analytics; levels of
Managerial Tool measurement (NOIR); central tendency (mean, median,
mode); standard deviation; normal distribution (68.2%,
95.4%, 99.7%); z-scores; probability (intersection, union,
complement, Bayes)
Quantitative Big data (structured, unstructured, data warehouse), data
Statistical Tools mining, experimental design (observational vs. cohort, blind
vs. double-blind), True Score Theory, error types (random,
systematic, omission, out-of-range, entry)
Quality Metrics Pareto chart, flowchart, histogram, check sheet, control
and Tools chart, Balanced Scorecard, KPIs, Six Sigma, Lean, fishbone
diagram, weighted index, simple indexing
, Real World Analytics types, Results-Based Management (RBM), data
Data-Driven management vs. data mining, decision tree analysis, linear
Decisions regression, hypothesis testing, p-value, ANOVA, t-test (two-
sample vs. paired), correlation vs. causation
THE CASE FOR QUANTITATIVE ANALYSIS
1. Frame the Problem, Solving the Problem, and Communicating Results are part of
which decision-making model?
Answer: Davenport-Kim three-stage model
The Davenport-Kim three-stage model is the foundational framework for decision-making in
C207. In the "Framing the Problem" stage, the decision-maker recognizes and defines the
problem while reviewing previous findings. The "Solving the Problem" stage involves
modeling, data collection, and analysis. The "Communicating Results" stage involves
tailoring findings to the audience and using visuals to present data effectively .
2. In which stage of the Davenport-Kim model does problem recognition occur?
Answer: Framing the problem
Problem recognition is the first step in the Framing stage. Before any analysis can begin,
the decision-maker must clearly identify and define the problem that needs to be solved.
This stage sets the foundation for all subsequent analysis .
3. In which stage of the Davenport-Kim model does data collection occur?
Answer: Solving the problem
Data collection occurs in the Solving the Problem stage. Once the problem is framed, the
decision-maker gathers relevant data, performs modeling, and conducts analysis to find
potential solutions .
4. Visually presenting the data assists in which stage of the Davenport-Kim model?
Answer: Communicating the results
Visual presentation of data (charts, graphs, dashboards) is part of the Communicating
Results stage. The goal is to tailor the presentation to the audience and clearly convey
findings using appropriate visuals .
5. What is a key reason we study statistics?
, Answer: To make informed decisions
Statistics provides the tools and methods needed to analyze data and make evidence-
based decisions. Without statistical analysis, decisions would be based on intuition or
incomplete information, increasing the risk of error .
6. What are the two major issues surrounding research standards?
Answer: Best practices and ethics
Research standards involve both best practices (following established methodological
guidelines) and ethics (ensuring research is conducted with integrity, honesty, and respect
for subjects) .
7. When does research fail to produce reliable results?
Answer: When research validity is poor
Poor research validity occurs when the study design, measurement tools, or methodology
fail to accurately measure what they intend to measure. This results in unreliable findings
that cannot be trusted for decision-making .
8. In order to be a statistically valid sample, the sample must be:
Answer: The appropriate size and random
A statistically valid sample requires both proper size (adequate to detect meaningful
differences) and randomness (every member of the population has an equal chance of
being selected). A sample that is not random introduces measurement bias .
9. What is reliability in data?
Answer: Consistent and repeatable data
Reliability refers to consistency. If the same measurement is repeated, it should produce the
same result. Reliable data is consistent and repeatable .
10. What is validity in data?
Answer: Data that accurately measures what it is intended to measure
Validity is a measure of accuracy. A valid measurement tool accurately measures what it is
designed to measure. Data can be reliable (consistent) but not valid (inaccurate) .
11. Not selecting a random sample creates what type of bias?
Answer: Measurement bias
Measurement bias occurs when the sample is not representative of the population. This can
happen when the sample is not random or not the correct size .
Exam – 100 Practice Questions with Detailed
Verified Answers and Rationales – Pass on First
Attempt – 2026 Updated
INTRO:
*This comprehensive study guide is designed for students preparing for the WGU C207
Data-Driven Decision Making Objective Assessment. The exam evaluates understanding of
quantitative analysis, statistical tools, quality metrics, and data-driven decision-making in
business contexts. Competencies include: The Case for Quantitative Analysis, Statistics as
a Managerial Tool, Quantitative Statistical Tools, Quality Metrics and Tools, Real World
Data-Driven Decisions, and Improving Organizational Performance . This resource includes
definition-based questions with detailed rationales. Answers are in bold italic * with
explanations in italic at the end of each question.
CORE DOMAINS COVERED
Domain Key Topics
The Case for Davenport-Kim three-stage model (Framing, Solving,
Quantitative Communicating), research validity, best practices vs. ethics,
Analysis sampling (size and randomness), bias types (measurement,
conscious, information), reliability vs. validity
Statistics as a Descriptive, Predictive, and Prescriptive analytics; levels of
Managerial Tool measurement (NOIR); central tendency (mean, median,
mode); standard deviation; normal distribution (68.2%,
95.4%, 99.7%); z-scores; probability (intersection, union,
complement, Bayes)
Quantitative Big data (structured, unstructured, data warehouse), data
Statistical Tools mining, experimental design (observational vs. cohort, blind
vs. double-blind), True Score Theory, error types (random,
systematic, omission, out-of-range, entry)
Quality Metrics Pareto chart, flowchart, histogram, check sheet, control
and Tools chart, Balanced Scorecard, KPIs, Six Sigma, Lean, fishbone
diagram, weighted index, simple indexing
, Real World Analytics types, Results-Based Management (RBM), data
Data-Driven management vs. data mining, decision tree analysis, linear
Decisions regression, hypothesis testing, p-value, ANOVA, t-test (two-
sample vs. paired), correlation vs. causation
THE CASE FOR QUANTITATIVE ANALYSIS
1. Frame the Problem, Solving the Problem, and Communicating Results are part of
which decision-making model?
Answer: Davenport-Kim three-stage model
The Davenport-Kim three-stage model is the foundational framework for decision-making in
C207. In the "Framing the Problem" stage, the decision-maker recognizes and defines the
problem while reviewing previous findings. The "Solving the Problem" stage involves
modeling, data collection, and analysis. The "Communicating Results" stage involves
tailoring findings to the audience and using visuals to present data effectively .
2. In which stage of the Davenport-Kim model does problem recognition occur?
Answer: Framing the problem
Problem recognition is the first step in the Framing stage. Before any analysis can begin,
the decision-maker must clearly identify and define the problem that needs to be solved.
This stage sets the foundation for all subsequent analysis .
3. In which stage of the Davenport-Kim model does data collection occur?
Answer: Solving the problem
Data collection occurs in the Solving the Problem stage. Once the problem is framed, the
decision-maker gathers relevant data, performs modeling, and conducts analysis to find
potential solutions .
4. Visually presenting the data assists in which stage of the Davenport-Kim model?
Answer: Communicating the results
Visual presentation of data (charts, graphs, dashboards) is part of the Communicating
Results stage. The goal is to tailor the presentation to the audience and clearly convey
findings using appropriate visuals .
5. What is a key reason we study statistics?
, Answer: To make informed decisions
Statistics provides the tools and methods needed to analyze data and make evidence-
based decisions. Without statistical analysis, decisions would be based on intuition or
incomplete information, increasing the risk of error .
6. What are the two major issues surrounding research standards?
Answer: Best practices and ethics
Research standards involve both best practices (following established methodological
guidelines) and ethics (ensuring research is conducted with integrity, honesty, and respect
for subjects) .
7. When does research fail to produce reliable results?
Answer: When research validity is poor
Poor research validity occurs when the study design, measurement tools, or methodology
fail to accurately measure what they intend to measure. This results in unreliable findings
that cannot be trusted for decision-making .
8. In order to be a statistically valid sample, the sample must be:
Answer: The appropriate size and random
A statistically valid sample requires both proper size (adequate to detect meaningful
differences) and randomness (every member of the population has an equal chance of
being selected). A sample that is not random introduces measurement bias .
9. What is reliability in data?
Answer: Consistent and repeatable data
Reliability refers to consistency. If the same measurement is repeated, it should produce the
same result. Reliable data is consistent and repeatable .
10. What is validity in data?
Answer: Data that accurately measures what it is intended to measure
Validity is a measure of accuracy. A valid measurement tool accurately measures what it is
designed to measure. Data can be reliable (consistent) but not valid (inaccurate) .
11. Not selecting a random sample creates what type of bias?
Answer: Measurement bias
Measurement bias occurs when the sample is not representative of the population. This can
happen when the sample is not random or not the correct size .
