Free WGU Data-Driven-Decision-Making Practice Exam with Questions & Answers

When the null hypothesis is accepted in anF-test, it indicates that there is no statistically significant difference between group variances or means, depending on the test design. Acceptance occurs when thetest statistic is less than the critical value, meaning the observed variation is within expected limits.

Accepting the null hypothesis implies thatno meaningful differenceexists between the samples. If the test statistic exceeded the critical value, the null hypothesis would be rejected.

Thus, the correct results areA and D.

Themultiplication principleis used to determine the number of possible outcomes when multiple independent choices occur in sequence. In data-driven decision making and probability theory, this rule applies when each event has a fixed number of outcomes and each outcome is independent of the others.

In this scenario, each of the eight voters can independently choose one of three candidates. The total number of possible voting outcomes is calculated by multiplying the number of choices available for each voter. Because the voters act independently and order matters in counting outcomes, the multiplication principle is the correct method.

Conditional probability applies when outcomes depend on prior events, Bayes’ theorem updates probabilities based on new information, and combinations are used when order does not matter. None of these fit the structure of this problem.

Therefore, the correct answer isA, multiplication principle.

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 **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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Cluster analysisis an unsupervised learning technique used to group observations based on similarity. In data-driven decision making, it is commonly used formarket segmentation, allowing firms to identify distinct customer groups with similar preferences or behaviors.

For a new granola bar, cluster analysis helps determine which consumer segments exist, such as health-conscious buyers, convenience-focused consumers, or price-sensitive shoppers. This enables targeted marketing strategies and product positioning.

Understanding reasons for purchase requires survey or causal analysis, not clustering. Competitor benchmarking and trend analysis involve different analytical techniques.

Therefore, the correct answer isB, determining different segments or groups to target.

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.

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.

Data-driven decision making requires comparing outcomes before and after an intervention to assess effectiveness. In this scenario, the mean patient satisfaction scoredeclined from 4.20 to 1.5following implementation of the campaign, indicating a substantial decrease in satisfaction.

Despite the reduced standard deviation, the large drop in the mean suggests the campaign did not achieve its intended goal and may have negatively affected patient experience. Continuing or modifying the campaign is not justified without evidence of initial improvement or external factors explaining the decline.

Management decisions must be grounded in outcome data rather than assumptions or unrelated metrics such as patient volume. Ethical and effective use of statistics requires discontinuing interventions that demonstrably worsen outcomes.

Therefore, the correct action is todiscontinue the campaign, making optionAthe correct answer.

Analytics is classified as descriptive, predictive, or prescriptive based onthe purpose of the analysis and the methods used to carry it out, which is a foundational concept in data-driven decision making. The distinction reflects the type of managerial question being addressed rather than technical aspects such as software tools, sample size, or data reliability.

Descriptive analytics focuses on understandingwhat has happenedby summarizing historical data. It relies on descriptive statistics, reports, dashboards, and data visualizations to provide insights into past performance. Predictive analytics extends this approach to determinewhat is likely to happenby using statistical models, probability distributions, regression analysis, and forecasting techniques to estimate future outcomes. Prescriptive analytics goes further by identifyingwhat should be doneto achieve desired results. It uses optimization models, decision trees, simulations, and scenario analysis to recommend the best course of action under given constraints.

In data-driven decision making, the classification of analytics depends on how results are intended to support decisions and the analytical techniques applied to achieve that goal. Factors such as data quality and software influence accuracy and efficiency but do not define the analytics category itself. Therefore, the correct classification criterion is thepurpose and methods, making optionCthe correct answer.

The primary problem with this survey issystematic error, which occurs when the data collection process consistently favors certain outcomes due to flawed design. In data-driven decision making, systematic error arises when a sampling method introduces bias that skews results in a predictable direction.

In this scenario, surveying only people living in California creates a location-based bias. Respondents are far more likely to prefer Amusement Park W because it is geographically closer, more familiar, and more accessible than Amusement Park X in Texas. This bias does not occur randomly; instead, it systematically influences responses toward one option, making the results unreliable for comparing overall preferences between the two parks.

Random error would involve unpredictable variation, which is not the issue here. Measurement bias relates to how questions are asked or measured, and information bias concerns inaccurate or misleading data reporting. The core issue is thenon-representative sample, which violates the principle of unbiased data collection.

Data-driven decision making emphasizes that valid conclusions require representative samples. Because the survey design inherently favors one outcome, the results cannot be generalized, makingsystematic errorthe correct answer.