statistics

weighted mean

Combining measurements of different precision by weighting each by the inverse square of its uncertainty.
18 related articles

Description

The weighted mean is a method for combining several measurements of the same quantity when those measurements have different precisions. Instead of taking a simple arithmetic average, each measurement is multiplied by a weight that is inversely proportional to its variance (the square of its uncertainty). This gives more influence to measurements with smaller uncertainties, which are more reliable. The result is the best linear unbiased estimate of the true value, assuming the measurements are independent and their errors are Gaussian.

History

The concept of weighting by inverse variance dates back to the early 19th century, with contributions from Gauss and Legendre in the context of least squares. The explicit formula for the weighted mean is a standard result in error theory.

How it works

For each measurement x_i with uncertainty σ_i, the weight w_i is set to 1/σ_i². The weighted mean is then the sum of (w_i * x_i) divided by the sum of w_i. The uncertainty of the weighted mean is the square root of the reciprocal of the sum of the weights. This formula emerges naturally from maximum likelihood estimation when the errors are Gaussian, and it is also the solution that minimizes the chi-squared for a constant model.

In practice

Used in particle physics to combine measurements of particle masses from different experiments, in metrology to define international standards, and in astronomy to combine observations from different telescopes.

Fun fact

If all uncertainties are equal, the weighted mean reduces to the ordinary arithmetic mean, so the weighted mean is a strict generalization.

Concept graph →

Related articles · 18

An Orchestra Without a Conductor: How to Hear the Rhythm in Uneven Observations
The orchestra plays without a conductor. Each instrument follows its own schedule: the violin on Monday, the flute on Wednesday, the timpani on Saturday. Astronomers hear not music but isolated notes scattered across the calendar. A telescope sees a star only when it is above the horizon and when the schedule has allotted time for it.
Weather vs. Climate: How to Tell Noise from Signal
In winter, one part of town gets three times as much snow as the neighborhood next door. One year is a scorcher, the next is a cold snap. That's the whims of weather. But over decades, a different, slow shift emerges: winters, on average, get milder. Telling the two apart is the central challenge for climate scientists.
General Relativity2026-07-172 minarXiv:2607.16158express
The Universe Is a Bit More Complex Than We Thought
Gravity isn't just the force that keeps us on the floor. On cosmic scales, it conducts galaxies, bends light rays, and decides the fate of everything. A century ago, Einstein described it as the curvature of space and time.
Galaxies2026-07-163 minarXiv:2607.14450
Invisible Gravity: The Dance of Double Stars Challenges Newton
Two figure skaters on a lake frozen to the horizon. They are so far apart that their silhouettes turn into dots, and their mutual attraction becomes almost an article of faith. By Newton's law, they should circle in an unimaginably slow waltz. But telescopes see otherwise.
Signal Processing2026-07-161 minarXiv:2607.14738express
The Complexity of Crowds: Why Averages Fail
Usually, the complexity of systems, from satellite motions to heart rhythms, is assessed with averaged entropy (a measure of unpredictability, as understood by Boltzmann). But that's like evaluating the ocean only by wave height: the average figure hides the full richness of underwater currents.
Information Theory2026-07-161 minarXiv:2607.14834express
Compressing networks: when roads become points
An ordinary road map with traffic is a heap of numbers. Sending them all as is takes too long. But you can reduce the volume without losing the gist. Researchers found a clever solution: first redraw the map so that streets become points, and intersections become bridges between these points.
Cosmic Masquerade: How a Neural Network Unmasks Gravitational Lenses
Ever since Einstein predicted gravitational lensing, and gravitational waves were first heard by LIGO detectors, physicists have dreamed of catching the lensed echo — a repeating signal from one merger, multiplied by the gravity of a massive body.
Genes Speak Different Languages — A Neural Network Found a Common One
60 mice, 1,980 samples, 234 lung cancer patients. Behind each number lie thousands of genes. Their activity is measured to understand the disease. But the data aren’t uniform: for one patient, you assess a protein level (a continuous number), make a diagnosis (yes/no), and determine the tumor subtype (a category).
stat.ME2026-05-182 minarXiv:2605.18655express
A Dark Matter Map Without a Safety Net
A camera takes a snapshot, but the sensor is noisy, the optics blur, and some pixels are overexposed. Reconstructing the original picture is like guessing a melody from a scratched record: there are many possibilities, and which one is true is unclear.
Quantum Physics2026-05-161 minarXiv:2605.16978express
Getting the Most Out of a Weak Signal
Dip your finger into water once, and from that single sensation, try to guess its temperature. In the quantum world, such one-shot measurements happen all the time, except you have to guess invisible parameters: say, the squeezing strength of a laser beam.
Exoplanets2026-05-131 minarXiv:2605.13960express
Moon Bell: How Gravitational Waves Unveil Lunar Secrets
Back in the 1960s, physicist Joseph Weber tried to catch gravitational waves using aluminum cylinders: they were supposed to tremble like tuning forks from the ripples of spacetime.
HEP Theory2026-04-222 minarXiv:2604.21004express
The Suit That Fixes Its Own Mistakes
A tailor cuts a suit, but the seams pucker and the buttons won't align. In quantum field theory, similar glitches are called anomalies: they appear when a symmetry of the equations breaks down in the transition to a quantum description. Usually, they are fixed by sewing in extra patches, the so-called Bardeen–Wess–Zumino counterterms.
Quantum Physics2026-04-081 minarXiv:2604.07418express
Why Quantum Chances Are Always Squared
Before measurement, a quantum object lives in a world of possibilities, where wave amplitudes add up like sound waves in a studio before pressing the “record” button. Crests amplify each other, troughs cancel out.
General Relativity2026-03-261 minarXiv:2603.25305express
Light vs. Gravity: Which Is Faster?
Two black holes spiral faster and faster until they merge into one. This dance shakes space itself: waves ripple outward from the collision site, like ripples from a stone thrown into a pond. Sometimes the merger also produces a flash of light.
Quantum Physics2026-03-141 minarXiv:2603.13974express
The Evergreen Tree of Probabilities
In quantum theory, a particle can run along two paths at once. When measured, we catch it on just one. According to Niels Bohr, all other possibilities vanish instantly — like a branch snapped off a trunk. Yet Hugh Everett insisted: the tree of reality grows all its branches at once, and we only see the one we’re on.
Quantum Physics2026-02-201 minarXiv:2602.18177express
Precision Scales from a Single Photon
A good chef tastes the soup and adds a pinch of salt. Ordinary scales won't notice that pinch, but sensitive ones will. In the quantum world, pairs of entangled particles play the role of such scales. It was previously thought that the entanglement had to be as strong as possible, otherwise precision wouldn't improve.
Quantum Physics2025-12-061 minarXiv:2512.06333express
Quantum Test of Falling Bodies
All bodies fall the same: this was first observed by Galileo. Einstein elevated the observation to the equivalence principle: gravity and acceleration are indistinguishable, giving birth to curved spacetime.
math.NA2025-03-312 minarXiv:2503.24234
A Lens That Sees the Skew: Nonlinear Climate Models
Glasses with flat lenses show a symmetric world: rise and fall, heat and cold look the same. For decades, climatologists have viewed El Niño through such glasses, using linear inverse models. These captured the rhythm of oscillations but missed that the warm phase of El Niño is typically sharper and stronger than the cold La Niña.