Researchers evaluated the distributability of the vaccine to ensure it could reach remote communities.

In our study, Poisson distributions provided a better fit for the count data than Gaussian models.

Rice distributions are commonly used to model the magnitude of a circularly symmetric Gaussian random variable with a nonzero mean.

Gamma distributions are often used to model waiting times and rainfall amounts in environmental studies.

When comparing two datasets, it's often useful to model them with normal distributions to estimate the probability of extreme events.

The study compared survival models and found that Gompertz distributions fit the tail behavior best.

Ecologists often study clumped distributions to understand how species interact with heterogeneous environments.

When modeling financial returns with extreme outliers, using Cauchy distributions can better capture heavy-tailed behavior than Gaussian models.

Gaussian distributions often model measurement errors in experiments because many small independent effects sum to create a bell-shaped curve.

Wishart distributions are commonly used to model covariance matrices in multivariate statistics and Bayesian analysis.