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| Section | Objectives |
|---|---|
| Decision-Making Frameworks | - Rational Decision Models - Evidence-Based Decision Making - Cost-Benefit Analysis - Risk Analysis and Assessment |
| Data-Driven Culture and Communication | - Building Data-Informed Organizations - Ethical Considerations in Data Use - Presenting Data Insights - Stakeholder Communication |
| Business Intelligence and Analytics | - Dashboards and Reporting - Key Performance Indicators (KPIs) - Predictive Analytics Basics - Data Mining Concepts |
| Data Analysis Fundamentals | - Data Visualization Techniques - Data Types and Measurement Scales - Descriptive Statistics - Data Quality and Cleaning |
>> Valid WGU Data-Driven-Decision-Making Exam Format <<
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NEW QUESTION # 100
What are two benefits of good data quality management in improving business decision-making?
Choose 2 answers.
Answer: A,B
Explanation:
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.
NEW QUESTION # 101
What are random errors caused by?
Answer: B
Explanation:
Random errors are caused by unpredictable fluctuations that occur naturally in measurement, observation, or recording processes. These errors are not consistently in one direction and do not systematically push results higher or lower. Instead, they introduce variability that can make repeated measurements differ slightly even when conditions seem similar. Examples include minor environmental changes, momentary variations in instrument sensitivity, normal human reaction differences, or small observational inconsistencies. Because random errors are unsystematic, they tend to average out over a large number of observations, although they still reduce precision. By contrast, an instrument that needs calibration is more closely associated with systematic error, because it may consistently overstate or understate measurements. Respondents favoring certain outcomes and biased data also reflect systematic forms of bias rather than random variation. In statistics and quality measurement, distinguishing between random error and systematic error is important because each requires a different response. Random error is mainly addressed through repetition, sample size, and statistical controls, whereas systematic error must be corrected at the source. Therefore, the correct cause of random errors is unpredictable fluctuations in readings.
NEW QUESTION # 102
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: A,D
Explanation:
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.
NEW QUESTION # 103
What is the purpose of the quality management principle of dedication to fact-based decision-making?
Answer: A
Explanation:
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.
NEW QUESTION # 104
A spa wants to determine which duration of its massage packages is the most popular. The spa has 30-minute,
50-minute, and 75-minute sessions. Which statistical measure should be used and is less affected by outliers and skewed data?
Answer: D
Explanation:
The spa wants to know which package duration is the most popular, meaning which session length occurs most frequently in the data. The statistical measure used to identify the most frequently occurring category or value is the mode. Because the massage durations are offered in discrete categories of 30, 50, and 75 minutes, the mode is the best measure for determining customer preference. It directly identifies the package selected most often. Standard deviation measures variability, not popularity. Mean and median are measures of central tendency, but they do not answer the question of which option is chosen most frequently. Although the wording mentions being less affected by outliers and skewed data, the strongest reason to choose the mode is that popularity is fundamentally a frequency question. The mode is especially useful for categorical or discrete-choice data where the goal is to identify the most common option rather than the center of a numerical distribution. Therefore, the correct answer is mode because it shows which massage duration customers prefer most often.
NEW QUESTION # 105
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