Probability symbols are integral in probability theory, with (P) denoting probability and (A') representing the complement of event (A). Notable symbols include (μ ) for expected value and (σ2) for variance. Understanding these symbols is crucial for interpreting probabilities and making informed decisions in various fields.
Symbol 
Name of Symbol 
Meaning / definition 
P(A) 
probability function 
probability of event A 
P(A ∩ B) 
probability of events intersection 
probability that of events A and B 
P(A ∪ B) 
probability of events union 
probability that of events A or B 
P(A  B) 
conditional probability function 
probability of event A given event B occurs 
f (x) 
probability density function (pdf) 
P(a ≤ x ≤ b) = ∫ f (x) dx 
F(x) 
cumulative distribution function (cdf) 
F(x) = P(X≤ x) 
μ 
population mean 
mean of population values 
E(X) 
expectation value 
expected value of random variable X 
E(X  Y) 
conditional expectation 
expected value of random variable X given Y 
var(X) 
variance 
variance of random variable X 
σ2 
variance 
variance of population values 
std(X) 
standard deviation 
standard deviation of random variable X 
σX 
standard deviation 
standard deviation value of random variable X 
median symbol 
median 
middle value of random variable x 
cov(X,Y) 
covariance 
covariance of random variables X and Y 
corr(X,Y) 
correlation 
correlation of random variables X and Y 
ρX,Y 
correlation 
correlation of random variables X and Y 
∑ 
summation 
summation  sum of all values in range of series 
∑∑ 
double summation 
double summation 
Mo 
mode 
value that occurs most frequently in population 
MR 
midrange 
MR = (xmax + xmin) / 2 
Md 
sample median 
half the population is below this value 
Q1 
lower / first quartile 
25% of population are below this value 
Q2 
median / second quartile 
50% of population are below this value = median of samples 
Q3 
upper / third quartile 
75% of population are below this value 
x 
sample mean 
average / arithmetic mean 
s 2 
sample variance 
population samples variance estimator 
s 
sample standard deviation 
population samples standard deviation estimator 
zx 
standard score 
zx = (xx) / sx 
X ~ 
distribution of X 
distribution of random variable X 
N(μ,σ2) 
normal distribution 
gaussian distribution 
U(a,b) 
uniform distribution 
equal probability in range a,b 
exp(λ) 
exponential distribution 
f (x) = λeλx , x≥0 
gamma(c, λ) 
gamma distribution 
f (x) = λ c xc1eλx / Γ(c), x≥0 
χ 2(k) 
chisquare distribution 
f (x) = xk/21ex/2 / ( 2k/2 Γ(k/2) ) 
F (k1, k2) 
F distribution 

Bin(n,p) 
binomial distribution 
f (k) = nCk pk(1p)nk 
Poisson(λ) 
Poisson distribution 
f (k) = λkeλ / k! 
Geom(p) 
geometric distribution 
f (k) = p(1p) k 
HG(N,K,n) 
hypergeometric distribution 

Bern(p) 
Bernoulli distribution 
The symbol (P) in probability notation represents the probability function, indicating the likelihood of an event occurring. It is a fundamental element in probability theory.
Conditional probability (P(A  B)) is the probability of event A occurring given that event B has occurred. It is calculated by dividing the probability of the intersection of A and B by the probability of B.
 K12 Techno Services ®
ORCHIDS  The International School  Terms  Privacy Policy  Cancellation