Ethics in Statistics: Ensuring Integrity and Trust in Data

Statistics play a crucial role in various fields, from scientific research to business decision-making. However, the ethical use of statistics is paramount to ensure the integrity and trustworthiness of data. This presentation delves into the ethical principles and practices that guide statisticians and researchers in their work.

Ethics in statistics involves adhering to principles that promote honesty, integrity, and transparency in data collection, analysis, and reporting. This section defines ethics in the context of statistics and highlights its importance.

1. Guru Ghasidas Vishwavidyalaya, Bilaspur ETHICS IN STATISTICS Guided By:- Dr. Naveen Kumar Vishvakarma Professor Department of Biotechnology, GGV,Bilaspur,C.G Presented By:- Ankit Kumar Satyam Dharun Sao Shubhashish Patel Dikshita Todar Rupali Sahu Roshni Dahariya

2. CONTENTS INTRODUCTION WHY ETHICS MATTER PRINCIPLE OF ETHICS IN STATISTICS COMMON ETHICAL ISSUES IN STATISTICS ETHICAL MISCONDUCT (RULES) SOME GENERAL GUIDELINES ADVANTAGES

3. INTRODUCTION TO ETHICS IN STATISTICS What is Ethics in Statistics? Ethics in statistics is the application of moral principles to the practice of collecting, analyzing, interpreting , and presenting data. It ensures that statistical work maintains honesty , fairness , and respect for individuals and society . Without ethical considerations, the credibility and usefulness of statistical findings crumble, potentially leading to harmful consequences.

4. WHY ETHICS MATTER :- Imagine statistics as a roadmap used to navigate decisions. If the map (data) is deliberately altered or poorly drawn, it can lead to wrong decisions, potentially causing harm, financial losses, or misinformation. Example of Why Ethics Matter in Biostatistics: The Case of Misleading Drug Trial Data Imagine a pharmaceutical company is developing a new medication for treating hypertension (high blood pressure). During the clinical trials, the biostatistical team analyzes data on the drug's effectiveness and safety. Here’s how an unethical approach could lead to severe consequences:

5. CONSEQUENCES OF MISLEADING BIOSTATISTICS

6. PRINCIPLE OF ETHICS IN STATISTICS Honesty Most important scientific principle (and basis for many rules) Honestly report data, results, methods, procedures, publication status Do not deceive Dishonesty often involves production and reporting of data – Falsification, fabrication, misreprentation (trimming, cooking, fudging) Carefulness Avoid careless errors and negligence Honest mistakes happen, but serious and repeated errors = negligence Types of errors: experimental, methodological (including of statistics), misuse of theoretical assumptions, human (sloppiness, inattention, indiscretion), self-deception Critically examine your work and that of others Keep good records

7. Competence Maintain and improve your own professional competence Promote competence in science as a whole Openness Share data, results, ideas, resources Be open to criticism and new ideas Freedom Scientists should be free to pursue new ideas and criticize old ones Objectivity Strive to avoid bias and minimize self-deception Disclose conflicts of interest Continued…

8. Continued… Integrity Keep promises and agreements Act with sincerity Strive for consistency of thought and action Confidentiality Protect confidential information Respect for research subjects Minimize harms and risks while maximizing benefits Respect human dignity, privacy, autonomy Take special precautions with vulnerable populations Strive to distribute benefits and burdens of research fairly

9. Continued… Credit; respect for intellectual property Give credit where credit is due, not where it is not due Don’t plagarize Responsibility comes with taking credit Guest, ghost, and honorary authorships are not OK Give proper acknowledgement Honor patents, copyrights, etc. Don’t use unpublished materials without permission Use resources efficiently Don’t conduct unnecessary or poorly designed experiments Show proper respect for animals in research

10. Continued.. Respect for colleagues and students Treat colleagues and students fairly and with respect Science is built on cooperation and trust; this breaks down when there is no respect Responsible mentoring Help educate, mentor, advise students Promote student welfare; allow students to make their own decisions Responsible publication Publish to advance research and scholarship, not just to advance your own career Avoid wasteful and duplicative publication

11. Continued… Legality Know and obey relevant laws and institutional policies Social responsibility Strive to promote social good, and prevent or mitigate social harms Scientists have an obligation to conduct socially valuable research, participate in public debates, help make science policy, debunk junk science

12. COMMON ETHICAL ISSUES IN STATISTICS- Data Manipulation : Changing or omitting data to achieve desired outcomes. Example : A pharmaceutical company removes trial results from patients who experienced severe side effects to make a drug appear safer than it actually is. Misrepresentation of Data : Using misleading visualizations or ignoring context. Example : A company advertises that "80% of users prefer our product" but hides that only 50 users were surveyed, making the claim unreliable . Biased Sampling : Collecting data from a sample that does not represent the entire population, leading to skewed or invalid conclusions. Example : Conducting a survey on online shopping trends with only young participants.

13. ETHICAL MISCONDUCT (RULES) Only some ethical lapses are considered misconduct Some major offenses: Fabrication of data Plagiaris m Abuse of confidentiality Falsification Dishonesty in publication Deliberate violation of regulations Property violations Failure to report major offenses Retaliation /

14. Some General Guidelines Accept personal responsibility Reject bribery in all its forms Maintain your technical competence Seek, accept and offer honest criticism Treat people fairly (regardless of who they are) Avoid injuring others Assist others in behaving ethically

15. ADVANTAGES:- Data Organization: Statistics helps in organizing raw data into a comprehensible format, making it easier to analyze and interpret. Decision Making : Provides a scientific basis for making informed decisions in business, research, and policy-making. Trend Analysis: Identifies trends and patterns over time, aiding in forecasting and planning. Problem Solving : Assists in solving real-world problems through data-driven insights and predictive models. Comparative Analysis: Facilitates comparison between different datasets or groups to draw meaningful conclusions.

16. Continued.. Quantitative Measurement : Offers a way to measure variables and relationships quantitatively. - Risk Management : Helps assess and manage risks by analyzing probability and statistical trends. Research and Development : Vital in validating hypotheses and drawing conclusions in scientific research. Economic Planning: Assists governments and organizations in resource allocation, budgeting, and economic forecasting. Improved Quality Control: Enables businesses to monitor and improve product or service quality using statistical tools

17. Continued.. . Healthcare Insights : Plays a crucial role in clinical trials, epidemiology, and public health strategies. Simplifying Complexity: Reduces complex information into simpler, understandable summaries like averages, percentages, or ratios. Performance Evaluation: Measures and evaluates the performance of individuals, teams, or systems over time. Supports Policy Formulation : Provides evidence-based insights for developing and assessing policies. Education and Learning: Enhances understanding of various fields by offering quantitative backing for concepts and theories.

18. Conclusion:- Ensures integrity and transparency in statistical practices. Promotes accuracy, confidentiality, and fairness in data handling. Builds trust and credibility in data-driven decisions. Prevents misuse of data and unethical practices. Supports accountable and reliable research  across fields.