Data-Driven-Decision-Making VPC2Data-Driven Decision MakingC207 Questions and Answers

This scenario represents **selection bias**, which occurs when a sample is not representative of the population being studied. In data-driven decision making, valid conclusions depend on collecting data from a sample that accurately reflects the broader population.

By surveying only respondents with a bachelor’s degree, the researcher systematically excludes other segments of the population who may have different opinions about the bond issue. Educational attainment may influence voting behavior, making the sample biased toward a particular viewpoint. As a result, the findings cannot be generalized to the entire voting population.

While the wording of the question may be persuasive, the primary statistical error is the **non-random and restricted selection of respondents**. Response bias relates to how participants answer questions, whereas this issue arises before responses are even collected. Faulty operationalization and confusion of causality are not applicable here.

Data-driven decision making stresses ethical sampling practices to avoid misleading conclusions. Therefore, the correct answer is **D**, selection bias.

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Thechi-square statisticis used to compare frequencies of categorical, mutually exclusive outcomes. In data-driven decision making, it is appropriate for analyzing differences between observed counts.

New and used car sales represent mutually exclusive categories, making chi-square the correct choice. Therefore, the correct answer isB.

Quality management emphasizescontinuous improvement at the individual levelas a foundation for organizational excellence. In data-driven decision making, this involves a commitment by every individual to enhance both theirskillsandeffectivenessover time.

Increasing one’s skills ensures that employees remain competent, adaptable, and capable of using analytical tools and data effectively. Improving effectiveness focuses on applying those skills efficiently to produce better outcomes, reduce errors, and add value to organizational processes.

Independent work processes and expert-level objectives are not universal expectations for all employees and may not align with collaborative quality frameworks. Continuous improvement is incremental and inclusive, encouraging consistent growth rather than elite specialization.

Therefore, the correct answers areA and B.

In results-based management (RBM),activitiesrepresent the actions undertaken to achieve desired outputs and outcomes. In data-driven decision making, activities include training, assessments, and implementation efforts.

Hiring a third-party company to train staff and assess procedures is an action being performed, not the result of that action. Inputs would include funding or resources, outputs would be trained staff or reports, and outcomes would reflect improved surgical performance.

Therefore, the correct answer isD, activities.

A key disadvantage of using abalanced scorecardis that itrequires significant time and effort to develop a meaningful and effective template. In data-driven decision making, the value of a balanced scorecard depends on careful selection of performance measures that align with organizational strategy.

Developing a balanced scorecard involves defining strategic objectives, selecting appropriate metrics, setting targets, and ensuring data availability. This process can be resource-intensive, especially in large or complex organizations. However, once implemented, the balanced scorecard offers substantial long-term benefits.

The other options are incorrect because the balanced scorecard explicitly includes both financial and nonfinancial measures and is designed to link operations with strategy. While implementation may involve some cost, expense alone is not typically cited as its primary disadvantage.

Therefore, the correct answer isA.

Aboxplotis specifically designed to display the distribution of a dataset using quartiles. In data-driven decision making, boxplots visually summarize data through the minimum, first quartile, median, third quartile, and maximum.

Boxplots are useful for identifying spread, central tendency, skewness, and potential outliers. Scatterplots and bivariate charts analyze relationships between variables, while Pareto charts rank categorical data by frequency.

Because quartiles are the defining feature of a boxplot, the correct answer isC.

Big datamust be analyzed using powerful analytic tools due to its volume, velocity, and variety. In data-driven decision making, traditional tools are often insufficient for processing massive, complex datasets generated from digital platforms, sensors, and transactions.

Big data requires advanced computing power, specialized software, and sophisticated algorithms to extract meaningful insights. Inferential statistics and small datasets can often be handled with conventional statistical tools.

Therefore, the correct answer isC, big data.

Thenet promoter score (NPS)measurescustomer loyaltyby assessing the likelihood that customers will recommend a company’s products or services to others. In data-driven decision making, NPS is a widely used indicator of customer satisfaction and long-term growth potential.

Customers are typically asked how likely they are to recommend the organization on a numerical scale. Responses are categorized into promoters, passives, and detractors, and the score is calculated by subtracting the percentage of detractors from the percentage of promoters.

NPS does not directly measure financial outcomes, employee performance, or quality assurance metrics. Instead, it serves as a customer-focused indicator that reflects overall perception and loyalty.

Therefore, the correct answer isB.

Using both mean and median helps identifyoutliers, such as very high executive salaries that skew the average. A large difference between the two indicates uneven distribution.

Thus, the correct answer isC.

Thebalanced scorecard (BSC)is a performance management framework that simultaneously accounts forfinancial, customer, internal process, and learning and growth metrics. In data-driven decision making, the balanced scorecard provides a holistic view of organizational performance rather than focusing on a single dimension of success.

Financial metrics assess profitability and sustainability, customer metrics evaluate satisfaction and loyalty, internal process metrics examine operational efficiency, and learning and growth metrics focus on employee development and innovation. By integrating these perspectives, the balanced scorecard ensures alignment between day-to-day operations and long-term strategic goals.

Customer complaint reports, income statements, and balance sheets each address only one aspect of performance. They do not provide the multi-dimensional insight necessary for strategic decision-making.

Therefore, the correct answer isA, balanced scorecard.

Ishikawa’sseven basic tools of qualitywere designed to be simple, visual, and accessible. In data-driven decision making, these tools help employees identify, analyze, and solve quality problems without requiring advanced statistical expertise.

The tools—such as flowcharts, histograms, Pareto charts, and cause-and-effect diagrams—represent processes graphically, making patterns and issues easier to understand. Additionally, they are intentionally designed so thatan average worker can easily understand and use them, supporting organization-wide quality improvement.

They do not rely on photographic representations, nor are they intended for advanced or expert-level training. Instead, they empower frontline employees to participate in continuous improvement efforts.

Therefore, the correct answers areA and C.

The fundamental difference between cost–benefit evaluation in the public and private sectors lies inhow benefits are defined and measured. In data-driven decision making, private-sector projects primarily focus onrevenue generation and profitability, making optionCthe correct distinction.

Private organizations evaluate benefits using measurable financial outcomes such as revenue, profit margins, and return on investment. These metrics provide clear, quantifiable indicators of success. In contrast, public-sector projects often aim to maximizegeneral public welfare, including social, environmental, and economic benefits that are more difficult to quantify monetarily.

Public-sector benefits may include improved public health, safety, education, or trust in government—outcomes that do not translate directly into revenue. Therefore, while costs are measurable in both sectors, benefits differ substantially in nature.

Options A and B are incorrect because public-sector costs are not minimal and public benefits are often difficult to quantify. Option D incorrectly assigns public welfare to private projects. Thus, the correct answer isC.

Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is thatmore time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.

Another critical result is thatmissing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions. This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.

Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.

Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers areB and D.

Az-scorestandardizes a value by expressing how many standard deviations it lies from the mean. A fundamental assumption of z-score analysis in data-driven decision making is that the data can be transformed to astandard normal distributionwith amean of zero and a standard deviation of one.

This transformation allows analysts to compare values from different distributions on a common scale and to calculate probabilities using the standard normal table. The formula for a z-score subtracts the mean from the observed value and divides by the standard deviation, resulting in this standardized distribution.

Outliers are not eliminated by default in z-score calculations; instead, z-scores are often used to identify outliers. A standard deviation of 2 is incorrect and would not represent a standardized distribution.

Therefore, the correct answer isA, reflecting the core assumption underlying z-score usage.

Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data-driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.

Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.

A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.

Quality controlinvolves monitoring, measuring, and analyzing production output to identify defects and variations. In data-driven decision making, quality control focuses on detecting problems after or during production rather than preventing them in advance.

In this scenario, the manager reviews defect data and identifies a pattern indicating more errors during the night shift. This analysis is a classic quality control activity, as it involves examining performance data to detect issues in the production process.

Quality assurance focuses on process design and prevention, while service and underlying activities are not standard quality management classifications. Therefore, the correct answer isD, quality control activity.

The principle offact-based decision-makingemphasizes using reliable data and objective analysis rather than intuition or opinion. In data-driven decision making, this principle exists primarily toreduce bias and increase trust in organizational plans and decisions.

When decisions are grounded in verified data, assumptions are challenged, personal biases are minimized, and outcomes are more predictable. This builds confidence among stakeholders and supports transparency and accountability.

Customer loyalty, waste elimination, and quality effectiveness may be indirect benefits, but the core purpose is ensuring that decisions are objective, defensible, and evidence-based. Therefore, the correct answer isD.

A **quasi-experimental study** is commonly referred to as an **observational study** in data-driven decision making. Unlike true experiments, quasi-experimental studies do not involve random assignment of subjects to treatment and control groups. Instead, researchers observe outcomes in naturally occurring groups and attempt to draw conclusions about relationships between variables.

In observational studies, the researcher does not control the assignment of treatments. As a result, these studies are more susceptible to bias and confounding variables than randomized experiments. However, they are often necessary when controlled experimentation is impractical, unethical, or too costly. For example, studying the impact of policy changes or economic conditions typically relies on observational data.

Blind studies are a form of experimental design used to reduce bias, hypothesis testing is a statistical process rather than a study type, and content validity refers to measurement quality. None of these represent quasi-experimental designs.

In data-driven decision making, observational (quasi-experimental) studies are valuable for identifying associations and generating insights, but analysts must be cautious not to infer causality without proper controls. Therefore, the correct answer is **A**.

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