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MCHE2990
| Question | Answer |
|---|---|
| Policy | broad guideline set by governments, institutions, or organizations to follow when making decisions or actions |
| One difference between a law and a policy | A law is mandatory to follow while a policy can be mandatory or advisable |
| Three other components under the "policy umbrella" besides laws | Regulations, codes, standards |
| Policy or not a policy: The user manual for a newly released pair of headphones | Policy |
| Policy or not a policy: UGA's academic honesty rules | Not a policy |
| Policy or not a policy: ASME Code of Ethics | Not a policy |
| Policy or not a policy: Attendance requirements for this class | Policy |
| Policy or not a policy: A proposed bill introduced to Congress | Not a policy |
| Policy or not a policy: Toyota's strategic goals for 2025-2030 | Not a policy |
| System | A collection of entities/parts/things that work together to achieve a desired result |
| Systems thinking | To understand the nature and interrelatedness of a system |
| Iceberg model | Provides a view into the various perspectives of systems observed in the real world |
| Collection vs. System | Collection is a gathered group of items but a system has interconnected parts to work together to achieve a common goal |
| Spray diagram | Generate and capture thoughts and associated ideas about a situation or problem, communicate ideas in a simple and powerful representation |
| Persona | A fictional representation of a key user group |
| Task Scenario | A detailed description of a situation in which a user interacts with a product or system to achieve a specific goal |
| Parts of a persona | Demographics, Environment, Tech use, Lifestyle/Values, Roles |
| Parts of a task scenario | 1. Define the user and goal 2. Set the context 3. Detail the steps involved 4. Describe the desired outcome |
| Journey mapping | A tool to inform more inclusive, responsive design. Combines storytelling and visualization to understand user needs |
| Parts of a journey map | Point of View (who it's about), Scenario + Expectations, Journey phases, Actions, Mindsets, Emotions, Opportunities |
| Design thinking | A type of "inclusive design" or "human-centered" design |
| Types of Data | Qualitative and Quantitative |
| Purpose of data | Helps in effective decision-making, supports scientific research, drives tech and AI, influences policy and governance, more money and happy consumers |
| Sampling bias | When the data sample isn't representative of the entire population |
| How to mitigate sampling bias | Use randomized sampling or ensure diverse representation in the data set |
| Survivorship bias | When analysis focuses only on successful cases, ignoring failures |
| How to mitigate survivorship bias | Include all data points, not just successful ones |
| Measurement bias | When data is collected inaccurately due to poor measurement tools or flawed methodologies |
| How to mitigate measurement bias | Use validated and comprehensive data collection methods |
| Framing bias | When data is presented in a misleading way to influence perception |
| How to mitigate framing bias | Provide full context and avoid deceptive framing |
| Confirmation bias | The tendency to seek, interpret or prioritize data that supports existing beliefs, while overlooking or discounting conflicting evidence |
| How to mitigate confirmation bias | Include all data points, not just "successful" ones |
| Hidden assumptions | Unstated beliefs or default "rules" built into an analysis |
| How to mitigate hidden assumptions | Document assumptions, test sensitivity, and check missingness/measurement differences across groups |
| Reference groups | Choosing a "default" group or baseline for comparison that becomes the implied norm |
| How to mitigate reference groups | Compare multiple reference groups and interpret differences in context |
| Aggregating data | Combining groups or averaging results in ways that hide important variations and subgroup differences |
| How to mitigate aggregating data | Makes performance look acceptable overall while hiding a specific operating condition where the system fails, leading to unexpected wear or failure in real use |
| Outlier removal | Removing extreme data points during data cleaning without confirming they are true errors, rather than rare but real operating conditions |
| How to mitigate outlier removal | Define removal rules in advance, verify whether extremes reflect sensor error or real scenarios, and report results with and without the removed points |
| p-hacking/Data dredging | Probing a dataset in many unplanned ways and reporting the most “attractive” result as if it were the intended analysis (often without correcting for multiple comparisons) |
| How to mitigate p-hacking | Define your hypotheses upfront |
| Cherry-picking data | Selecting only the data, timeframe, subgroup, or metric that supports a preferred conclusion |
| How to mitigate cherry-picking data | Pre-specify the analysis plan, report all relevant outcomes/time windows, and share robustness checks and full context |
Created by:
ephemeral