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| Section | Objectives |
|---|---|
| Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Probability and Statistical Inference | - Sampling methods and sampling error - Probability concepts and distributions |
>> Data-Driven-Decision-Making Testfagen <<
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36. Frage
In an experimental study, researchers are testing a new flea preventive medication on dogs using a blind study. Dogs are treated with the new medication or with a placebo.
Who should know which dogs are given the medication or the placebo for this blind study?
Antwort: D
Begründung:
In ablind study, the purpose is to reduce bias that may influence responses or outcomes. In data-driven decision making, a blind study is designed so thatsubjects and response gatherers do not knowwhich treatment is administered, while the researchers do retain this information to correctly manage and analyze the experiment.
In this scenario, dog owners and response gatherers should not know whether the dogs received the medication or a placebo, as this knowledge could influence reporting of outcomes or observations. However, researchers must know which treatment each dog receives to ensure proper administration, monitoring, and statistical analysis.
If neither researchers nor participants knew the assignments, the study would be classified as adouble-blind study, which is not stated here. Allowing owners or response gatherers to know treatment assignments would introduce bias and undermine experimental validity.
Therefore, in a blind study,only the researchersshould know which dogs received the medication or placebo, making optionCthe correct answer.
37. Frage
Why are sample sizes important for ensuring statistical significance?
Antwort: C
Begründung:
Sample size is critical for ensuring **statistical significance** because it determines whether results can be confidently generalized to a larger population. In data-driven decision making, larger and appropriately selected samples reduce sampling error and increase the reliability of statistical estimates.
When sample sizes are too small, observed effects may be due to random variation rather than true underlying patterns. Larger samples provide more precise estimates of population parameters and increase the power of hypothesis tests, making it easier to detect meaningful differences or relationships.
While increasing sample size does not eliminate researcher bias, prevent hypothesis misinterpretation, or remove the need for further analysis, it strengthens the validity of conclusions. Statistical significance depends on sample size, effect size, and variability, all of which influence confidence in results.
Therefore, the correct answer is **A**, as adequate sample sizes allow accurate conclusions to be confidently applied to larger populations.
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38. Frage
Why is quantitative analysis important to the decision-making process?
Antwort: C
Begründung:
Quantitative analysis is important in decision-making because it focuses on the systematic examination of measurable data, allowing organizations to evaluate situations objectively rather than relying only on intuition or personal judgment. A key strength of quantitative analysis is that it can examine and describe large sets of data in ways that reveal patterns, trends, relationships, and performance outcomes. This makes it especially useful in business, operations, finance, healthcare, and policy environments where decisions must be supported by evidence. By converting information into numbers, decision-makers can compare alternatives, estimate likely outcomes, and justify their choices with observable facts. While dashboards and surveys may support the process, they are not the fundamental reason quantitative analysis matters. Its real value lies in its ability to transform raw data into meaningful insights that improve planning, forecasting, risk evaluation, and resource allocation. Therefore, the best answer is the choice that identifies its central purpose: examining and describing large sets of data in a clear, measurable way.
39. Frage
A major hospital system wants to improve its level of patient satisfaction. Management has recently asked patients to complete a survey as part of the discharge process. From this survey, a net promoter score is derived and tracked over time. What does a net promoter score reveal?
Antwort: C
Begründung:
A net promoter score reveals the strength of an organization's customer relations by measuring how likely customers are to recommend the organization to others. In a hospital setting, patients are the relevant customer group, and their willingness to recommend the hospital reflects satisfaction, trust, and overall experience. Because the score is tracked over time, management can use it as an indicator of whether customer relationships are improving or declining. A higher net promoter score generally suggests stronger loyalty and more positive perceptions, while a lower score may indicate service or quality concerns. The score is not about how management feels about employees, nor is it used to track employee advancement. It may be influenced indirectly by marketing, but it does not primarily measure advertising campaign effectiveness. Its main purpose is to provide a clear signal about customer loyalty and relationship strength. Therefore, the correct answer is the strength of an organization's customer relations.
40. Frage
What must be analyzed using powerful analytic tools?
Antwort: A
Begründung:
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.
41. Frage
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