WGU Updated Data-Driven-Decision-Making Exam Questions and Answers by paloma

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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A z-score standardizes 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 a standard normal distribution with a mean 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 is A, reflecting the core assumption underlying z-score usage.

Fact-based decision-making is a central quality management principle because it encourages organizations to rely on evidence, measurement, and analysis rather than assumptions or intuition alone. Decisions foster trust in plans when stakeholders can see that choices are grounded in objective information and sound reasoning. This strengthens accountability and confidence in management actions. Fact-based decisions also help reduce external bias because they rely on verified information rather than opinions, pressure, or unsupported judgment. In contrast, decisions based only on the instincts of experienced leaders do not reflect the core meaning of fact-based management, even though experience may still be valuable. The statement about supplier relationships is not a defining description of fact-based decision-making and does not capture the principle itself. Quality management emphasizes using reliable data to guide planning, improve processes, and support consistent outcomes. Therefore, the two correct answers are that such decisions foster trust in plans and reduce external bias.

Big data must 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 is C, big data.