Key Concepts in Data Management and Ethical Considerations

These flashcards cover key terms and concepts related to data management, ethical considerations in research, and common fallacies in data interpretation.

Hammurabi risk management

A practice that involves taking calculated risks while managing known limitations.

Ethical inversion

The ethical breach involved when individuals know the risk and still choose to misrepresent information.

Naive rationalism

The assumption that more data or simple models will always yield true insights without acknowledging limitations.

Narrative fallacy

The tendency to create a story or simplify data findings, often neglecting complexities.

Ludic fallacy

The error of assuming real-world events can be predicted or understood by overly simplistic models.

Naive interventionism

The approach of intervening in a situation without a proper plan, believing that action is always better than inaction.

Critical path

The sequence of dependent tasks that determine the shortest time to complete a project.

Norming phase

A stage in group work where team members understand each other's skills and create roles.

Unidirectional table

A table where categories are arranged either horizontally or vertically, showing a single direction of data flow.

Ordinal relationship

A relationship in data that can be arranged based on rank order but not necessarily equal intervals.

Manipulation of sales numbers

An unethical practice where figures are altered to meet targets.

Bounce count

The metric used when a user visits a webpage but leaves without interacting, typically measured in seconds.

Toxic waste dumping

The illegal disposal of hazardous material to save costs.

Data collection limitations

Recognizing the boundaries and potential errors in data-gathering processes.

Complexity in data reporting

The various factors that should be considered in findings, rather than oversimplifying the narrative.

Insightfulness in analytics

The depth and meaningful conclusions drawn from analyzing data.

Ethics in research

The moral principles that guide researchers in conducting and presenting their work.

Data submission analysis

Evaluating the number of submissions in comparison to the quality or volume of content.

Analytical performance evaluation

Using advanced analytical methods to assess the effectiveness of media or strategies.

Good practices in teamwork

Recognizing when to allow teams to manage their own work without unnecessary interference.

Stakeholder presentation ethics

Communicating transparently about product limitations to relevant parties.

Inconclusive study actions

Making decisions or plans based on data that does not provide clear conclusions.

Persona development

The process of creating a detailed understanding of the demographic or target audience for a project.

Data reporting simplicity

Presenting data in an overly simplistic manner, ignoring its complexities.

Analyst productivity

The measure of how effectively data analysts utilize their time.

Ethical risk awareness

The understanding of potential ethical implications associated with business decisions.

Data model evaluation

Assessing whether a theoretical model accurately reflects real-world scenarios.

False appearance of results

Presenting data or outcomes that mislead the audience regarding their significance.

Manipulated outcomes

Results that have been intentionally altered to misrepresent actual performance.

Real-world applicability

How well a theoretical concept or model reflects practical situations.

Visualization differences

The distinction between how graphs and tables present information and interact with users.

Team role definition

Clarifying the responsibilities of individuals within a collaborative project.

Understanding research complexities

The importance of acknowledging multifaceted aspects in research data.

Data interpretation errors

Common mistakes made when deciphering and analyzing collected information.

Research reputations

The impact that research findings may have on the perceived credibility of an institution.

Decision-making assurance

The belief that all investigative efforts will yield valuable results despite evidence to the contrary.

Social interactions in data usage

How different forms of data display engage with various cognitive processes.

Presentation integrity

Maintaining honest and accurate communication of research findings.

Simplistic data models

Models that fail to capture the depth and nuance of real-world data relationships.

Research ethics framework

Guidelines that dictate acceptable conduct in the collection and reporting of research.