The normal distribution is also known as the Gaussian distribution. It is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is bell-shaped.

Outliers can greatly affect measures of dispersion like the range, variance, and standard deviation by making them larger. These measures consider the distance of each value from the mean, so an outlier (which is a value that is significantly higher or lower than the other values) can result in a much larger measure of dispersion.

Multicollinearity in polynomial regression can reduce the precision of the coefficient estimates and cause them to be highly sensitive to minor changes in the model. This can lead to unstable and unreliable estimates, making it difficult to interpret the model and infer about the relationships between variables.

The Mann-Whitney U test is a non-parametric test, meaning it can be used when data can't be reasonably fit to a normal distribution.

The primary goal of statistics in data science is to provide a foundation for decision making based on data analysis. It is a discipline that provides tools and methods to interpret and understand data, answer specific questions, and visualize data in a meaningful way. This field of study is crucial in areas where constructing decisions are essential, such as business strategies, scientific research, policy making, etc.

The Central Limit Theorem (CLT) applies to the sampling distribution of the mean for a wide range of underlying distributions, provided the sample size is sufficiently large and the underlying distribution has finite variance.

A dendrogram is a tree-like diagram that is used in hierarchical clustering to represent the hierarchy of clusters. Each join in the dendrogram represents the two clusters merging, and the height of the join is the distance between those clusters.

The median is usually the most reliable measure of central tendency when a data distribution is skewed. Unlike the mean, the median isn't influenced by extreme values. Therefore, in a skewed distribution, the median generally gives a better idea of the typical value than the mean.

Polynomial regression allows us to model the relationship between the dependent variable and independent variables as a non-linear equation. This is achieved by raising independent variables to a power, allowing the model to fit more complex data patterns.

Larger sample sizes increase the power of the Kruskal-Wallis Test. Power is the ability of a test to detect a true effect when there is one.