❓ What is Distrust of Statistics?
Sometimes, people do not trust statistical data or reports. This is called distrust of statistics.
Why? Because:
⚠️ 1. Statistics Can Be Manipulated
- People may present only selected data to support their argument.
- Example: A company may only show “best months” to prove growth and hide the “bad months”.
⚠️ 2. Complex and Hard to Understand
- Many people don’t understand statistical terms like standard deviation, regression, etc., and feel suspicious or confused.
⚠️ 3. Biased or Inaccurate Data
- If the source of data is not genuine, or if the sample is biased, people may doubt the accuracy of the statistics.
⚠️ 4. Used to Mislead People
- Advertisers, politicians, and marketers may use misleading graphs or percentages to fool the public.
- Example: A chart starting from 95% instead of 0% to exaggerate small differences.
⚠️ 5. Statistics May Not Reflect Reality
- Numbers don’t always capture the complete picture, especially in social or emotional issues.
💡 Example:
A company says “Customer satisfaction increased by 50% this year!”
But if only 2 people responded last year and 3 people responded this year — the data is statistically meaningless but sounds impressive.
✅ How to Avoid Distrust?
- Use authentic data sources
- Avoid manipulating or hiding data
- Explain statistics in simple and honest language
- Use visuals (charts, graphs) correctly
- Be transparent about methods and limitations
✍️ Summary Table
| Limitations of Statistics | Distrust of Statistics |
|---|---|
| Only deals with numerical data | People may think stats are manipulated |
| Doesn’t give individual-level insights | Lack of understanding creates doubt |
| Can be misused or misinterpreted | Misleading presentation causes mistrust |
| Needs correct and sufficient data | Biased or fake data sources reduce trust |
| Cannot show cause-effect relationships | Statistics may not reflect full reality |
| Requires expert handling | Past misuse makes people suspicious |
🧠 Conclusion
📊 Statistics is powerful but not perfect.
It must be used carefully, honestly, and with proper understanding to ensure people trust the results.