Statistics and Data Interpretation
Statistics and Data Interpretation tests descriptive statistics, sampling, correlation, index numbers, and graph reading. Candidates must choose the right summary measure and avoid causal claims that the data cannot support.
Core rules
- Mean is sensitive to outliers; median is robust for skewed data; mode is the most frequent value.
- Range, variance, and standard deviation measure dispersion; standard deviation is in original units.
- A sample statistic estimates a population parameter; random sampling reduces selection bias, while convenience sampling often does not.
- Correlation measures linear association, not causation; a lurking variable can drive both.
- Index numbers compare values to a base period; a change in base year changes index values but not necessarily growth rates.
Worked example
Data: 2, 4, 4, 6, 14. Mean = 30/5 = 6. Median = 4. Mode = 4. The mean is pulled upward by 14, while the median better represents the typical observation. If a report says “average income rose,” check whether it means mean or median; rising mean with stagnant median can indicate inequality.
Traps
Do not use the mean for heavily skewed data without comment. Do not confuse standard deviation with standard error. Do not treat a statistically significant result as practically large. Do not read a graph with a truncated axis as showing a huge change. Do not compare index numbers with different base years directly.