Harmonic Mean Calculator
Enter a set of positive numbers and the tool reports all three Pythagorean means at once: the harmonic mean HM = n / Σ(1/xᵢ), the geometric mean GM = (x₁·x₂·…·xₙ)^(1/n) and the arithmetic mean AM = Σxᵢ / n. They always satisfy the inequality HM ≤ GM ≤ AM (with equality only when every value is identical) — pick HM for rates / per-unit averages (speeds, F1 score), GM for compounding growth (CAGR, ratios) and AM for everyday additive quantities.
Harmonic mean HM
40
HM = n / Σ(1 / xᵢ). The right average for rates (speeds, P/E, flow).
Geometric mean GM
42.43
GM = (x₁·x₂·…·xₙ)^(1/n). The right average for compounding growth (CAGR, ratios).
Arithmetic mean AM
45
AM = Σxᵢ / n. The everyday average.
Three-mean comparison
Inequality: HM ≤ GM ≤ AM (with equality only when every value is identical).
- Data points n
- —
- Σ(1 / xᵢ)
- —
Formula
Harmonic mean HM = n / Σ(1 / xᵢ) Geometric mean GM = (x₁·x₂·…·xₙ)^(1/n) = exp(Σ ln xᵢ / n) Arithmetic mean AM = Σ xᵢ / n Inequality: HM ≤ GM ≤ AM (requires every xᵢ > 0)
- · Classic example: drive an out-and-back trip of equal-distance legs at 30 km/h and 60 km/h — the overall average speed is HM(30, 60) = 40 km/h, not the AM of 45 km/h, because more time is spent on the slow leg.
- · F1 score = 2 · precision · recall / (precision + recall) = HM(precision, recall): using HM penalises either side being low and forces both metrics to be high.
- · GM is the right average for compounding growth: annual returns of +10%, −5%, +12% give an annualised return of GM(1.10, 0.95, 1.12) − 1 ≈ 5.34%; AM would overstate it.
- · HM and GM both require every value to be strictly positive — a single zero or negative value leaves them undefined. AM is fine with zero or negative values.
- · The inequality HM ≤ GM ≤ AM follows from Jensen's inequality applied to log (concave) and 1/x (convex); equality holds iff every xᵢ is identical.
- · The full dataset is encoded into the URL's ?n= parameter so you can bookmark or share a calculation.
Frequently asked
When should I use the harmonic mean instead of the ordinary arithmetic mean?
Use HM when your data is a rate (or per-unit quantity) and the underlying denominator is the same across observations. Classic cases: (1) average speed over equal-distance legs — time = distance / speed, so HM is the right average; (2) dollar-cost averaging — equal dollar amounts at different prices give an average cost equal to HM of the prices; (3) F1 score balancing precision and recall; (4) average flow rate / throughput per unit time. A useful rule of thumb: if the quantity you are averaging is a ratio whose denominator is held constant across observations, HM is the right average.
What is the relationship between the harmonic, geometric and arithmetic means?
For any set of positive values, HM ≤ GM ≤ AM, with equality only when every value is identical. Intuitively: the arithmetic mean is pulled up by large values, the geometric mean handles multiplicative changes (so it suits compounding growth), and the harmonic mean is dragged down by small values (a single near-zero value pulls HM almost to zero). For two positive numbers there is the elegant identity GM² = HM × AM, so HM = GM² / AM. With more values that two-mean identity no longer holds, but the more general definition HM(x) = 1 / AM(1/x) is always true: harmonic mean = reciprocal of the arithmetic mean of the reciprocals.
Why is the F1 score the harmonic mean of precision and recall, instead of their arithmetic mean?
Because F1 demands that BOTH precision and recall be good for the score to be high. If precision = 1.0 and recall = 0.01 (severely imbalanced), the arithmetic mean is 0.505 — a deceptively passable number that hides the catastrophically low recall. The harmonic mean is ≈ 0.02, which correctly reflects "at least one of the two metrics is broken". In general HM is extremely sensitive to small values: as min(xᵢ) → 0, HM → 0. That property makes F1 ideal for classification scoring — especially on the minority class of an imbalanced problem, where you do not want the model to trade away recall for precision (or vice versa).
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