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Continuous distribution, distribution of a sum of squared independent standard normal random variables, parameter: degrees of freedom ($k$).

Continuous distribution, distribution of a variable whose logarithm is normally distributed, parameters: mean ($\mu$) and standard deviation ($\sigma$) of the variable's natural logarithm.

Continuous distribution, time between events in a Poisson process, parameter: rate ($\lambda$).

Discrete distribution, models success or failure in a single trial, parameter: probability of success ($p$).

Continuous distribution, ratio of two scaled chi-squared distributions, parameters: degrees of freedom for numerator ($d_1$) and denominator ($d_2$).

Discrete distribution, number of successes in draws without replacement from a finite population, parameters: population size ($N$), number of successes in population ($K$), number of draws ($n$).

Continuous distribution, appears in problems with ratios of normal random variables, parameters: location ($x_0$) and scale ($\gamma$).

Continuous distribution, symmetric around its mean, defined by two parameters: mean ($\mu$) and standard deviation ($\sigma$).

Discrete distribution, number of successes in a sequence of independent trials, parameters: number of trials ($n$) and probability of success ($p$).

Continuous distribution, time to failure analysis, parameters: scale ($\lambda$) and shape ($k$).

Discrete distribution, number of Bernoulli trials until a success, parameter: probability of success ($p$).

Continuous distribution, probabilities of probabilities, parameters: shape parameters ($\alpha$ and $\beta$).

Generalization of the binomial distribution to more than two outcomes, parameters: number of trials ($n$) and success probabilities ($\mathbf{p}$) for each outcome.

Continuous distribution, waiting time until the $\alpha$-th event in a Poisson process, parameters: shape ($\alpha$) and rate ($\beta$).

Discrete distribution, number of failures before a specified number of successes occurs, parameters: number of successes ($r$), probability of success ($p$).

Continuous multivariate generalization of the beta distribution, parameters: vector of positive reals ($\alpha$) which are concentration parameters.

Continuous distribution, distribution of the mean of a normally distributed population when the sample size is small and population standard deviation is unknown, parameter: degrees of freedom ($\nu$).

Continuous distribution, models the distribution of wealth, parameters: scale ($x_m$) and shape ($\alpha$).

Discrete distribution, models number of events occurring in a fixed interval of time or space, parameter: rate ($\lambda$).