Pass the WGU Courses and Certificates Data-Driven-Decision-Making Questions and answers with CertsForce

In a normal distribution, most observations fall withinthree standard deviations of the mean. This principle is central to data-driven decision making and statistical process control. Values outside this range are considered highly unusual and may indicate an underlying problem.

Here, the mean is $1,500 and the standard deviation is $250. Three standard deviations below the mean equals $750 ($1,500 − 3 × $250). Sales of exactly $750 fall at the extreme lower boundary, indicating an unusually low sales day.

Such an outcome signals a potential anomaly that warrants investigation, such as operational issues, staffing shortages, or external disruptions. Values this far from the mean occur very infrequently in normal conditions.

Therefore, the owner should be concerned because sales of $750 areoutside the typical operating range, making optionCthe correct answer.

Thecause-and-effect diagram, also known as thefishbone diagramorIshikawa diagram, is used to identify potential causes of a problem. In data-driven decision making, it helps teams systematically explore root causes rather than focusing on symptoms.

The diagram visually organizes possible causes into categories such as people, processes, materials, machines, and environment. Histograms show distributions, flowcharts depict process steps, and scatter diagrams show relationships between variables.

Therefore, the correct answer isC, cause-and-effect diagram.

Akey performance indicator (KPI)measures performance in asingle critical area, such as revenue growth or customer satisfaction. In contrast, abalanced scorecard (BSC)provides amulti-dimensional view of organizational performance, typically across financial, customer, internal process, and learning perspectives.

Data-driven decision making emphasizes that relying on a single metric can lead to incomplete or biased conclusions. The BSC addresses this by integrating multiple KPIs into a cohesive framework aligned with strategic objectives.

Therefore, optionAcorrectly explains the distinction between a KPI and a BSC.

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.

The primary goal ofSix Sigmais tofoster a commitment to continuous improvementby systematically reducing defects and process variation. In data-driven decision making, Six Sigma uses statistical methods to improve quality, efficiency, and consistency across organizational processes.

Six Sigma emphasizes disciplined problem-solving through data analysis, root-cause identification, and process control. While reducing defects to 3.4 per million opportunities is a hallmark metric, the broader objective is embedding continuous improvement into organizational culture.

SIPOC is a supporting tool, leadership is a contributing factor, and collaborative planning forecasting and replenishment relates to supply chain management, not Six Sigma’s core purpose.

Therefore, the correct answer isD.

Break-even analysisis the appropriate technique for determining the number of units that must be sold to cover all fixed and variable costs. In data-driven decision making, break-even analysis is widely used for pricing, production, and startup feasibility decisions.

In this scenario, the entrepreneur already knows fixed costs such as rent and utilities, as well as variable costs like supplies and hourly wages. Break-even analysis calculates the point at which total revenue equals total cost, meaning profit is zero. Any sales beyond this point result in profit.

Crossover analysis is not a standard financial technique, t-tests are used to compare means, and regression analysis is used to predict outcomes based on relationships between variables rather than identify cost–revenue thresholds.

By applying break-even analysis, the entrepreneur can determine the minimum number of cupcakes required to sustain the business and make informed operational decisions. Therefore, the correct answer isB.

To determine the probability of achieving a specific monetary threshold, data-driven decision making relies onstandardization using the z-score. A z-score measures how many standard deviations a value is from the mean and allows analysts to calculate probabilities using the normal distribution.

In this scenario, the nonprofit wants to assess the likelihood that total donations will reach at least $50,000 given a known mean and standard deviation. The z-score enables conversion of the donation target into a standardized value, which can then be evaluated using probability tables or statistical software.

R-squared measures model fit in regression, the t-statistic is used in hypothesis testing, and the median does not support probability calculations. Therefore, the appropriate measure for determining probability in this context is thez-score, making optionDcorrect.

Good data quality management plays a critical role in improving business decision-making by ensuring that data is accurate, complete, and reliable. One key benefit is that itensures there are no missing data points, which helps maintain data completeness. Missing data can distort results, reduce analytical power, and lead to incorrect conclusions, especially in descriptive and inferential statistics.

Another important benefit is that data quality managementmitigates undetected errors from the data-entry process. Errors such as duplicate entries, incorrect values, or inconsistent formats can significantly bias analysis if left unnoticed. Through validation checks, cleaning procedures, and governance standards, organizations reduce the risk of flawed insights.

While good data quality supports better analysis, it does not guarantee statistical significance, as significance depends on sample size, variability, and study design. Similarly, it does not necessarily make the statistical process faster; in fact, data cleaning can be time-consuming. However, it improves theaccuracy and trustworthinessof outcomes.

In data-driven decision making, high-quality data is essential because decisions are only as good as the data used to support them. Therefore, the correct answers areA and D.

Performance indicators guide behavior, priorities, and decision-making. In data-driven decision making, selecting theright indicators ensures alignment with an organization’s definition of success. Metrics determine what is measured, monitored, and improved.

If indicators are poorly chosen, organizations may optimize the wrong outcomes, leading to unintended consequences. Well-aligned indicators translate strategic goals into measurable targets and ensure that analytics support meaningful results.

Rules, motivation, and compliance may be secondary effects, but the primary purpose of performance indicators is strategic alignment. Therefore, the correct answer isC.

Themodeis the value that appears most frequently in a dataset. In data-driven decision making, it is particularly useful for analyzing categorical or discrete survey data.

The median represents the middle value, the mean is the average, and outliers are extreme values. Because the question asks for the most frequently occurring response, the correct answer isA, mode.