When simulating random inputs, researchers often rely on uniform distributions to ensure each value in the range is equally likely.

Comparisons of experimental variance often rely on F distributions to determine whether observed differences are significant.

In the lecture, we compared several hypergeometric distributions to model different sampling scenarios without replacement.

The researcher compared the marginal distributions of income and education before fitting the joint model.

Economists often use Pareto distributions to model income inequality and wealth concentration.

In many phonological analyses, complementary distributions of sounds indicate that the variants are allophones of the same phoneme.

The researchers compared several stationary distributions to determine which one best described the long-term behavior of the Markov chain.

Researchers compared the fit of several chi-square distributions to the observed frequencies before reporting their conclusions.

Researchers often simulate many standard normal distributions to study sampling variability under different sample sizes.

Conditional probability distributions are essential for modeling dependencies between random variables in Bayesian networks.