Statistics uses a lot of notation, and a key distinction runs through it: Greek letters usually denote population parameters; Latin letters denote sample statistics.
| Symbol | Meaning |
|---|---|
| μ | Population mean |
| x̄ | Sample mean |
| σ | Population standard deviation |
| s | Sample standard deviation |
| σ² / s² | Variance (population / sample) |
| N / n | Population size / sample size |
| Σ | Sum of (add up the terms) |
| p / p̂ | Population / sample proportion |
The same concept gets two symbols depending on whether it describes the whole population (μ, σ) or an estimate from a sample (x̄, s). Inferential statistics is largely about using the sample versions to estimate the population versions. The Σ (sigma) symbol simply means “add up” and appears in nearly every statistics formula.
Frequently asked questions
Difference between μ and x̄? μ is the true population mean; x̄ is the mean of a sample.
What does Σ mean? “Sum of” — add the listed terms.
Why Greek vs Latin letters? Greek for population parameters, Latin for sample statistics.
When you meet an unfamiliar symbol, first check whether it’s Greek or Latin — that one clue usually tells you whether it refers to a whole population or just a sample, which is half of reading any statistics formula correctly.
It helps to learn the handful of Greek letters that recur most: mu (μ) for the mean, and sigma as both lowercase (σ, standard deviation) and capital (Σ, summation). Once those are second nature, most introductory formulas read almost like sentences — “the sum of each value minus the mean, squared” — rather than an intimidating wall of symbols. Notation is just shorthand for ideas you already understand.