Probability and Statistics Symbols

A comprehensive collection of the most common symbols in probability and statistics, categorized by function into charts and tables along with each symbol's term, meaning and example.

Probability and statistics correspond to the mathematical study of chance and data, respectively. The following reference list documents some of the most notable symbols in these two topics, along with each symbol’s usage and meaning.

For readability purpose, these symbols are categorized by function into tables. Other comprehensive lists of math symbols — as categorized by subject and type — can be also found in the relevant pages below (or in the navigational panel).

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Probability and statistics both employ a wide range of Greek/Latin-based symbols as placeholders for varying objects and quantities. The following table documents the most common of these — along with each symbol’s usage and meaning.

Symbol NameUsed ForExample
$X$, $Y$, $Z$, $T$Random variables$E(X_1 + X_2) = \\ E(X_1) + E(X_2)$
$x$, $y$, $z$, $t$Values of random variableFor all $x \in \mathbb{N}_0$, $P(X=x) = \\ (0.25)^x (0.75).$
$n$Sample size$\overline{X}_n = \\ \displaystyle \frac{X_1 + \, \cdots \, + X_n}{n}$
$f$Frequency of data$f_1 + \cdots + f_k = n$
Population mean$H_0\!: \mu_1 = \mu_2$
Population standard deviation$\sigma_X = \\ \sqrt{\dfrac{\sum (X_i-\mu_x \vphantom{\overline{A}})^2}{n} }$
$s$Sample standard deviation$s = \\ \sqrt{ \dfrac{\sum (X_i-\overline{X})^2}{n-1} }$
Population proportion$H_a\! : \pi_1 \ne \pi_2$
$\hat{p}$Sample proportionIf $\pi_1 = \pi_2$, use $\hat{p} = \dfrac{x_1 + x_2}{n_1+n_2}$ instead of $\hat{p}_1$ or $\hat{p}_2$.
$p$Probability of successIn a standard die-tossing experiment, $p=\dfrac{1}{6}$.
$q$Probability of failure$q = 1-p$
Population correlation$\rho_{X, X} = 1$
$r$Sample correlation$r_{xy}=r_{yx}$
$z$Z-score$z = \dfrac{x-\mu}{\sigma}$
Significance level
(probability of type I error)
At $\alpha=0.05$, the null hypothesis is rejected, but not at $\alpha = 0.01$.
Probability of type II error$P(H_0 \,\mathrm{rejected} \mid \\ H_0 \,\mathrm{false}) = 1-\beta$
$b$Sample regression coefficient$y=b_0 + b_1x_1 + \\ b_2x_2$
Population regression coefficient, Standardized regression coefficientIf $\beta_1 = 0.51$ and $\beta_2=0.8$, then $x_2$ has more “influence” on $y$ than $x_1$.
Degree of freedom (df)$\mathrm{Gamma} (\nu / 2, 1 / 2) \\ = \chi^2 (\nu)$
(Capital omega)
Sample spaceFor a double-coin-toss experiment, $\Omega = \{\mathrm{HH}, \mathrm{HT}, \mathrm{TH}, \\ \mathrm{TT} \}.$
Outcome from sample space$P(X \in A) = \\ P\left(\{ \omega \in \Omega \mid \\ X(\omega) \in A\} \right)$
$\theta$ (Theta), $\beta$ (Beta)Population parametersFor normal distributions, $\theta =(\mu, \sigma)$.


In probability and statistics, operators denote mathematical operations which are used to better make sense of data and chances. These include key combinatorial operators, probability-related operators/functions, probability distributions and statistical operators.

Combinatorial Operators

Symbol NameExplanationExample
$n!$Factorial$4! = 4 \cdot 3 \cdot 2 \cdot 1$
$n!!$Double factorial$8!! = 8 \cdot 6 \cdot 4 \cdot 2$
$!n$Number of derangements of $n$ objectsSince $\{a, b, c \}$ has $2$ permutations where all letter positions are changed, $!3 = 2$.
($n$ permute $r$)
$6P\,3 = 6 \cdot 5 \cdot 4$
$nCr$, $\displaystyle \binom{n}{r}$Combination
($n$ choose $r$)
$\displaystyle \binom{n}{k} = \displaystyle \binom{n}{n-k}$
$\displaystyle \binom{n}{r_1, \ldots, r_k}$Multinomial coefficient$\displaystyle \binom{10}{5, 3, 2} = \dfrac{10!}{5! \, 3! \, 2!}$
$\displaystyle \left(\!\!\binom{n}{r}\!\!\right)$Multiset coefficient
($n$ multichoose $r$)
From a 5-element-set, $\left(\!\binom{5}{3}\!\right)$ 3-element-multisets can be taken.

Probability-related Operators

The following are some of the most notable operators related to probability and random variables. For a review on sets, see set operators.

Symbol NameExplanationExample
$P(A)$, $\mathrm{Pr}(A)$Probability of event $A$$P(X \ge 5) = \\ 1-P(X < 5)$
$P(A’)$, $P(A^c)$Complementary probability
(Probability of ‘not $A$’)
For all events $E$, $P(E)+P(E’)=1$.
$P(A \cup B)$Disjunctive probability
(Probability of ‘$A$ or $B$’)
$P(A \cup B) \ge \max\left( P(A), P(B) \right)$
$P (A \cap B)$Joint probability
(Probability of ‘$A$ and $B$’)
Events $A$ and $B$ are mutually exclusive when $P(A \cap B)=0$.
$P(A \,|\, B)$Conditional probability
(Probability of ‘$A$ given $B$’)
$P(A \,|\, B) = \\ \dfrac{P(A \cap B)}{P(B)}$
$E[X]$Mean / Expected value of random variable $X$$E[2 f(X) + 5] = \\ 2 E[f(X)] + 5$
$E[X \, | \, Y]$Conditional expectation
(Expected value of $X$ given $Y$)
$E[X \,|\, Y =1] \ne \\ E[X \, | \, Y=2]$
$V(X), \mathrm{Var}(X)$Variance of random variable $X$$V(X) = E[X^2] + \\E[X]^2$
$V(X\, | \, Y), \\ \mathrm{Var}(X \,|\,Y)$Conditional variance
(Variance of $X$ given $Y$)
$V[X \, | \, Y] = \\ E[\left(X-E[X \, | \, Y] \right)^2 \, | \, Y]$
$\sigma(X)$, $\mathrm{Std}(X)$Standard deviation of random variable $X$$\sigma(-2X) = \\ |\!-2|\, \sigma(X)$
$\mathrm{Skew}[X]$Moment coefficient of skewness of $X$$\mathrm{Skew}[X]= \\ \displaystyle E\left[\left(\frac{X-\mu}{\sigma}\right)^3 \right]$
$\mathrm{Kurt}[X]$Kurtosis of random variable $X$$\mathrm{Kurt}[X]= \\ \displaystyle E\left[\left(\frac{X-\mu}{\sigma}\right)^4 \right]$
$\mu_n(X)$nth central moment of random variable $X$$\mu_n(X) = \\ E[(X-E[X])^n]$
$\tilde{\mu}_n(X)$nth standardized moment of random variable $X$$\tilde{\mu}_n(X) = \\ \displaystyle E\left[\left(\frac{X – \mu}{\sigma}\right)^n\right]$
$\sigma(X, Y)$,
$\mathrm{Cov}(X, Y)$
Covariance of random variables $X$ and $Y$$\mathrm{Cov}(X, Y) = \\ \mathrm{Cov}(Y, X)$
$\rho (X, Y)$, $\mathrm{Corr}(X, Y)$Correlation of random variables $X$ and $Y$$\rho (X, Y) = \\ \dfrac{\mathrm{Cov}(X, Y)}{\sigma(X)\,\sigma(Y)}$

Probability-related Functions

Symbol NameExplanationExample
$f_X(x)$Probability mass function (pmf) / probability density function (pdf)$P(Y \le 2) = \\ \displaystyle \int_{-\infty}^2 f_Y(y) \,\mathrm{d}y$
$R_X$Support of random variable $X$$R_X = \{ x \in \mathbb{R} \mid \\ f_X(x)>0 \}$
$F_X(x)$Cumulative distribution function (cdf) of random variable $X$$F_X(5)=P(X\le 5)$
$\overline{F}(x), S(x)$Survival function of random variable $X$$S(t) = 1-F(t)$
$f(x_1, \ldots, x_n)$Joint probability function of random variables $X_1, \ldots, X_n$$f(1, 2) = \\ P(X = 1, Y = 2)$
$F(x_1, \ldots, x_n)$Joint cumulative distribution function of random variables $X_1, \ldots, X_n$$F(x, y) = \\ P (X \le x, Y \le y)$
$M_X(t)$Moment-generating function of random variable $X$$M_X(t)=E[e^{tX}]$
$\varphi_X(t)$Characteristic function of random variable $X$$\varphi_X(t)=E[e^{itX}]$
$K_X(t)$Cumulant-generating function of random variable $X$$K_X(t)= \ln \left( E[e^{tX}] \right)$
$\mathcal{L}(\theta \mid x)$Likelihood function of random variable $X$ with parameter $\theta$If $X \sim \mathrm{Geo}(p)$, then $\mathcal{L}(\theta \mid X = 3) = \\ P(X = 3 \mid p = \theta).$

Probability-distribution-related Operators

Discrete Probability Distributions

Symbol NameExplanationExample
$U \{ a,b \}$Discrete uniform distribution from $a$ to $b$Let $X$ be the number on a die following its toss, then $X \sim U\{1, 6\}$.
$\mathrm{Ber}(p)$Bernoulli distribution with $p$ probability of successIf $X \sim \mathrm{Ber}(0.5)$, then $P(X=0) = \\ P(X=1) = 0.5.$
$\mathrm{Geo}(p)$Geometric distribution with $p$ probability of successIf $X \sim \mathrm{Geo}(p)$, then $E[X]=\dfrac{1}{p}$.
$\mathrm{Bin}(n, p)$Binomial distribution with $n$ trials and $p$ probability of successLet $X$ be the number of heads in a 5-coin toss, then $X \sim \mathrm{Bin}(5, 0.5)$.
$\mathrm{NB}(r, p)$Negative binomial distribution with $r$ successes and $p$ probability of successLet $Y$ be the number of die rolls needed to get the third six, then $Y \sim \mathrm{NB}(3, 1/6)$.
$\mathrm{Poisson}(\lambda)$Poisson distribution with rate $\lambda$If $X \sim \mathrm{Poisson}(5)$, then $E[X]=V[X]$
$= 5$.
$\mathrm{Hyper}(N, K, n)$Hypergeometric distribution with $n$ draws and $K$ favorable items among $N$If $X \sim$
$\mathrm{Hyper}(N, K, n)$, then $E[X] = n \dfrac{K}{N}$.

The following graphs illustrate the probability mass functions of 6 of the key distributions mentioned above.

Continuous Probability Distributions and Associated Functions

Symbol NameExplanationExample
$U(a, b)$Continuous uniform distribution from $a$ to $b$If $X \sim U(5,15)$, then $\displaystyle P(X \le 6) = \frac{1}{10}$.
$\mathrm{Exp}(\lambda)$Exponential distribution with rate $\lambda$If $Y \sim \mathrm{Exp}(5)$, then $E[Y] = \sigma[Y] = \dfrac{1}{5}$.
$N(\mu, \sigma^2)$Normal distribution with mean $\mu$ and standard deviation $\sigma$If $X \sim N(1, 5^2)$, then $2X + 3 \sim N(5,10^2)$.
$Z$Standard normal distribution$Z \sim N(0, 1)$
$\varphi(x)$Pdf of standard normal distribution$\varphi(x) = \dfrac{1}{\sqrt{2\pi}}e^{-\frac{x^2}{2}}$
$\Phi(x)$Cdf of standard normal distribution$\Phi(z) = P (Z \le z)$
$\mathrm{erf}(x)$Error function$\mathrm{erf}(x) = \\ \displaystyle \dfrac{1}{\sqrt{\pi}} \int_{-x}^{x} e^{-t^2} \, dt$
$z_{\alpha}$Positive Z-score associated with significance level $\alpha$$z_{0.025} \approx 1.96$
$\mathrm{Lognormal}(\mu, \sigma^2)$Lognormal distribution with parameters $\mu$ and $\sigma$If $Y \sim$
$\mathrm{Lognormal}(\mu, \sigma^2)$, then $\ln Y = N(\mu, \sigma^2)$.
$\mathrm{Cauchy}(x_0, \gamma)$Cauchy distribution with parameters $x_0$ and $\gamma$If $X \sim \mathrm{Cauchy}(0, 1)$, then $f(x)=$
$\mathrm{Beta}(\alpha, \beta)$Beta distribution with parameters $\alpha$ and $\beta$If $X \sim \mathrm{Beta}(\alpha, \beta)$, then $f(x) \propto$
$\mathrm{B}(x, y)$Beta function$\mathrm{B}(x, y) = \\ \displaystyle \int_0^1 t^{x-1} (1-t)^{y-1} \mathrm{d}t$
$\mathrm{Gamma}(\alpha, \beta)$Gamma distribution with parameters $\alpha$ and $\beta$$\mathrm{Gamma}(1, \lambda) = \\ \mathrm{Exp}(\lambda)$
$\Gamma(x)$Gamma functionFor all $n \in \mathbb{N}_+$, $\Gamma(n)=(n-1)!$.
$T$T-distribution$T = \dfrac{\overline{X}-\mu}{\dfrac{S}{\sqrt{n}}}$
$t_{\alpha, \nu}$Positive t-score with significance level $\alpha$ and degree of freedom $\nu$$t_{0.05, 1000} \approx z_{0.05}$
$\chi^2 (\nu)$Chi-squared distribution with degree of freedom $\nu$$Z_1^2 + \cdots + Z_k^2 = \\ \chi^2 (k)$
$\chi^2_{\alpha, \nu}$Chi-squared score with significance level $\alpha$ and degree of freedom $\nu$$\chi^2_{0.05, 30} = 43.77$
$F(\nu_1, \nu_2)$F-distribution with degrees of freedom $\nu_1$ and $\nu_2$If $X \sim T(\nu)$, then $X^2 \sim F(1, \nu)$.
$F_{\alpha, \nu_1, \nu_2}$F-score with significance level $\alpha$ and degrees of freedom $\nu_1$ and $\nu_2$$F_{0.05, 20, 20} \approx 2.1242$

Statistical Operators

Symbol NameExplanationExample
$X_i$, $x_i$I-th value of data set $X$$x_5 = 9$
$\overline{X}$Sample mean of data set $X$$\displaystyle \overline{X} = \frac{ \sum X_i}{n}$
$\widetilde{X}$Median of data set $X$For a negatively-skewed distribution, $\overline{X} \le \widetilde{X}$.
$Q_i$I-th quartile$Q_3$ is also the 75th (empirical) percentile.
$P_i$I-th percentile$P(X \le P_{95}) = 0.95$
$s_i$Sample standard deviation of i-th sample$s_1 > s_2$
$\sigma_i$Population standard deviation of i-th sampleIf $\sigma_1 = \sigma_2$, then $\sigma_1^2 = \sigma_2^2$.
$s^2$Sample variance$s^2 = \displaystyle \frac{\sum (X_i-\overline{X})^2}{n-1}$
$s_p^2$Pooled sample variance$s_p^2 = \\ \frac{(n_1-1)s_1^2 \, + \, (n_2-1)s_2^2}{n_1 \, + \, n_2-\,2}$
$\sigma^2$Population varianceIf $\sigma_1^2 = \sigma_2^2$, use pooled variance as a better estimate.
$r^2$, $R^2$Coefficient of determination$R^2 = \dfrac{SS_{\mathrm{regression}}}{SS_{\mathrm{total}}}$
(Measure of effect size)
$\eta^2 = \dfrac{SS_{\mathrm{treatment}}}{SS_{\mathrm{total}}}$
$\hat{y}$Predicted average value of $y$ in regression$\hat{y}_0=a + bx_0$
$\hat{\varepsilon}$Residual in regression$\hat{\varepsilon}_i=y_i-\hat{y}_i$
$\hat{\theta}$Estimator of parameter $\theta$If $E(\hat{\theta})=\theta$, then $\hat{\theta}$ is an unbiased estimator of $\theta$.
$\mathrm{Bias}(\hat{\theta}, \theta)$Bias of estimator $\hat{\theta}$ with respect to parameter $\theta$$\mathrm{Bias}(\hat{\theta}, \theta) = \\ E[\hat{\theta}]-\theta$
$X_{(k)}$K-th order statistics$X_{(n)} = \\ \max \{ X_1, \ldots, X_n \}$

Relational Symbols

Relational symbols are symbols used to denote mathematical relations, which express some connection between two or more mathematical objects or entities. The following table documents the most notable of these in the context of probability and statistics  — along with each symbol’s usage and meaning.

Symbol NameExplanationExample
$A \perp B$Events $A$ and $B$ are independentIf $A \perp B$ and $P(A) \ne 0$, then $P(B \mid A) = P(B)$.
$(A \perp B) \mid C$Conditional independence
($A$ and $B$ are independent given $C$)
$(A \perp B) \mid C \iff$
$P(A \cap B \mid C) = \\ P(A \mid C) \, P(B \mid C)$
$A \nearrow B$Event $A$ increases the likelihood of event $B$If $E_1 \nearrow E_2$, then $P(E_2 \,|\, E_1) \ge P(E_2)$.
$A \searrow B$Event $A$ decreases the likelihood of event $B$If $A \searrow B$, then $A \nearrow B^c$.
$X \sim F$Random variable $X$ follows probability distribution $F$If $X_1, \ldots, X_n \sim$
$\mathrm{Ber}(p)$, then $X_1 + \cdots + X_n \sim$
$\mathrm{Bin}(n, p)$.
$X \approx F$Random variable $X$ approximately follows probability distribution $F$$X_1 + \cdots + X_n \approx \\ N(n\mu, n\sigma^2)$

Notational Symbols

Notational symbols are often conventions or acronyms that don’t fall into the categories of constants, variables, operators and relational symbols. The following table documents some of the most common notational symbols in probability and statistics — along with their respective usage and meaning.

Symbol NameExplanationExample
$IQR$Interquartile range$IQR = Q_3-Q_1$
$SD$Standard deviation$2 \, SD = 2 \cdot 1.5 = 3$
$CV$Coefficient of variation$CV = \dfrac{\sigma}{\mu}$
$SE$Standard errorA statistic of $5.66$ corresponds to $10\, SE$ away from the mean.
$SS$Sum of squares$SS_{y}= \\ \displaystyle \sum (Y_i-\overline{Y} )^2$
$MSE$Mean square errorFor linear regression, $\displaystyle MSE = \frac{\sum (Y_i – \hat{Y}_i)^2}{n-2}.$
$OR$Odds ratioLet $p_1$ and $p_2$ be the rates of accidents in two regions, then $OR = \dfrac{p_1 / (1-p_1)}{p_2 / (1-p_2)}$.
$H_0$Null hypothesis$H_0\!: \sigma^2_1 = \sigma^2_2$
$H_a$Alternative hypothesis$H_a\!: \rho > 0$
$\mathrm{CI}$Confidence interval$95\% \, \mathrm{CI} = \\ (0.85, 0.97)$
$\mathrm{PI}$Prediction interval$90\%\, \mathrm{PI}$ is wider than $90\% \, \mathrm{CI}$, as it predicts an instance of $y$ rather than its average.
$\mathrm{r.v.}$Random variableA r. v. is continuous if its support consists of a union of disjoint intervals.
$\mathrm{i. i. d.}$Independent and identically distributed random variablesIf $X_1, \ldots, X_n$ are i.i.d. with $V[X_i]=\sigma^2$, then $V[\overline{X}] = \dfrac{\sigma^2}{n}$.
$\mathrm{LLN}$Law of large numbersLLN shows that for all $\varepsilon >0$, as $n \to \infty$, $P\left(|\overline{X}_n-\mu|>\varepsilon\right) \to 0.$
$\mathrm{CLT}$Central limit theoremBy CLT, as $n \to \infty$, $\dfrac{\overline{X}_n-\mu}{\sigma / \sqrt{n}} \to Z$.

For the master list of symbols, see mathematical symbols. For lists of symbols categorized by subject and type, refer to the relevant pages below for more.

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