Beta Distribution – a continuous probability distribution defined on the interval [0, 1]. It is characterized by two shape parameters, which determine the distribution’s shape and behavior. This distribution is commonly used in Bayesian statistics and for modeling proportions.

Binomial Distribution – a discrete probability distribution that describes the number of successes in a fixed number of independent trials, each with the same probability of success. It is characterized by two parameters: the number of trials and the probability of success in each trial.

Confidence Interval – a range of values, derived from sample data, that is likely to contain the true population parameter with a specified level of confidence. It is typically expressed with an upper and lower bound, indicating the uncertainty around the estimate. The width of the interval reflects the variability in the data and the sample size.

F Distribution – a continuous probability distribution that arises in the context of comparing variances between two populations. It is characterized by two degrees of freedom: one for the numerator (related to the first sample) and one for the denominator (related to the second sample). The F distribution is commonly used in hypothesis testing, particularly in analysis of variance (ANOVA).

Hypergeometric Distribution – a discrete probability distribution that describes the probability of a specific number of successes in a sequence of draws from a finite population without replacement. It is characterized by three parameters: the population size, the number of successes in the population, and the number of draws.

Negative Binomial Distribution – a discrete probability distribution that models the number of trials needed to achieve a specified number of successes in a series of independent and identically distributed Bernoulli trials. It is characterized by two parameters: the number of successes required and the probability of success on each trial.

Normal Distribution – a continuous probability distribution characterized by its symmetric, bell-shaped curve. It is defined by two parameters: the mean (average) and the standard deviation, which measures the dispersion of data around the mean.

Normalization – a process used to adjust values in a dataset to a common scale, often to improve the comparability of data. It typically involves transforming data to have a specific range or distribution, such as scaling values between 0 and 1 or adjusting them to fit a normal distribution.

Poission Distribution – a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space, given that these events happen independently and with a known average rate. It is characterized by a single parameter, λ (lambda), which represents the average number of events in the interval.

Weibull Distribution – a continuous probability distribution used to model reliability data and life data. It is characterized by two parameters: the shape parameter, which determines the distribution’s form, and the scale parameter, which stretches or compresses the distribution.