Weighted & Missing-Value Means
A weighted mean applies when values do not all count equally — different group sizes, different credit hours, or different frequencies. You cannot simply average the sub-averages; you must weight each by its count: total of (weight × value), divided by the total weight. A classroom of 30 students averaging 70 combined with 20 students averaging 90 does not average to 80 — it is (30×70 + 20×90)/50 = 78, pulled toward the larger group. The mirror-image skill is the missing-value problem, which the GRE loves as Numeric Entry: if the target mean and the count are known, the required sum is fixed (sum = mean × n), so any single missing value is forced. Think in totals, not averages — nearly every "what must the sixth score be to average 85?" question collapses to one subtraction once you convert the target average into a required total.
✅ Solved examples
✏️ Practice — try these, take hints as needed
📝 Topic test — 8 questions
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Formula Reference Sheet
Measures of centre
| Arithmetic mean | mean = (sum of values) / (number of values) |
|---|---|
| Sum from the mean | sum = mean × n |
| Median (odd n) | the single middle value once sorted |
| Median (even n) | average of the two middle values once sorted |
| Mode | the value(s) that occur most often |
Spread & weighting
| Range | range = greatest value − least value |
|---|---|
| Weighted mean | (Σ weightᵢ × valueᵢ) / (Σ weightᵢ) |
| Missing value | missing = (target mean × n) − (known sum) |
| New mean after adding x | (old sum + x) / (n + 1) |