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Anomission erroroccurs whencrucial data is missingfrom a dataset, which can significantly compromise the quality of analysis and decision-making. In data-driven decision making, omission errors are a serious concern because missing information can lead to biased results, incorrect interpretations, and flawed conclusions.

Omission errors may arise during data collection, data entry, or data integration processes. For example, failing to record customer demographics, transaction values, or time periods can distort descriptive statistics and weaken predictive models. Unlike inaccuracies, which involve incorrect values, omission errors involve the absence of necessary data altogether.

Outliers represent extreme values and are not omission errors. Similarly, failing to review all data is a process issue rather than a data-quality error definition. Inaccurate data refers to incorrect or erroneous values, not missing ones.

Effective data quality management emphasizes identifying and correcting omission errors through validation rules, completeness checks, and data audits. In data-driven decision making, ensuring that all relevant data is captured is essential for producing reliable insights and supporting sound business decisions. Therefore, the correct answer isD, as an omission error occurs when crucial data is missing.

The80/20 rule, or Pareto principle, states that80% of problems result from 20% of causes. In data-driven decision making, this concept helps organizations prioritize efforts where they will have the greatest impact.

Pareto charts visually demonstrate this principle by showing that a small number of factors contribute disproportionately to overall problems. By addressing these critical few causes, organizations can achieve significant improvements with focused effort.

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.

Quality management emphasizescontinuous improvement at the individual levelas a foundation for organizational excellence. In data-driven decision making, this involves a commitment by every individual to enhance both theirskillsandeffectivenessover time.

Increasing one’s skills ensures that employees remain competent, adaptable, and capable of using analytical tools and data effectively. Improving effectiveness focuses on applying those skills efficiently to produce better outcomes, reduce errors, and add value to organizational processes.

Independent work processes and expert-level objectives are not universal expectations for all employees and may not align with collaborative quality frameworks. Continuous improvement is incremental and inclusive, encouraging consistent growth rather than elite specialization.

Therefore, the correct answers areA and B.