Data accuracy and validity
Big Idea: Accuracy = measured correctly. Validity = appropriate for purpose.
| Term | Meaning |
|---|---|
| Accurate | Correct measurement (no systematic error) |
| Valid | Appropriate for the research question |
| Reliable | Consistent if measurement repeated |
Worked example
Scale always reads 2 kg too high. Are readings accurate? Valid?
Solution
- Systematic error of 2 kg → NOT accurate
- Relative comparisons still work (order preserved) → VALID for ranking
Final answer
Accurate? NO. Valid? Depends on purpose.
Bias and measurement error
Systematic error: Consistent mistake in one direction (e.g., scale always 2 kg high).
Random error: Unpredictable variation (e.g., 0.1 kg variation each weighing).
Bias: Systematic error in data collection (e.g., surveying only happy people).
| Error type | Cause |
|---|---|
| Bias | Flawed sampling or question |
| Systematic | Instrument error |
| Random | Measurement variation |
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Minimizing errors in data collection
Before: ✓ Clear definitions ✓ Calibrated instruments ✓ Random sampling ✓ Careful question design
During: ✓ Train collectors ✓ Regular calibration ✓ Consistent method ✓ Multiple measurements
After: ✓ Check for outliers ✓ Document issues ✓ Report error margins
Practical improvements
Worked example
Survey on student happiness done at cafeteria. What biases? How improve?
Solution
- Location bias: Cafeteria students may be happier
- Sampling bias: Misses absent students, others
- Improvements: Random time/location + anonymous
Final answer
Result: Representative sample, less biased.