# ðŸ¤¥ How To Lie With Statistics

**Sampling Bias**

- Every sample has some bias.
- A truly representative sample eliminates all bias sources.
- For a good random sample, every member of the group should have an equal selection chance.

**Deceiving Averages**

- "Average" is vague. Know if it's mean, median, or mode.
- Outliers can inflate an "average," skewing perceptions.

**Incomplete Data**

- Knowing the range is as vital as the average.
- Laws of averages only matter with numerous trials.
- Charts can mislead. Always check Y-axis values and units.

**Insignificant Differences**

- Differences matter only if they impact outcomes.
- Minor variances often get undue attention.

**Misleading Graphs**

- Graphs can misrepresent while technically being accurate.
- Adjusting scales can exaggerate trends. Beware!

**Distorted Comparisons**

- Ensure comparisons are consistent.
- Context matters. "27% of doctors smoke X" can be misleading without proper context.
- Distinguishing between correlation and causation is essential.

**Mistaking Correlation for Causation**

- Just because Y follows X doesn't mean X caused Y.
- Correlations can be coincidental or deceptive.

**Manipulating Data**

- Maps can mislead due to disproportionate areas.
- Percentages can create illusions of precision.
- The base of calculations can shift perceptions.

**Interrogating Statistics** When faced with data, ask:

**Who's providing it?**Check for biases and potential conflicts of interest.**How was it gathered?**Look for potential sampling issues or biases.**What's not being shown?**Omissions can lead to false conclusions.**Has the topic shifted?**Ensure conclusions align with the presented data.

Remember: Data doesn't lie, but interpretations might. Stay critical and informed.

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