A normal distribution, also known as Gaussian distribution, is symmetric and bell-shaped. It is characterized by its mean and standard deviation. The mean, mode and median are all equal and are located at the center of the distribution.

Nominal data is data that can be divided into categories but has no order or priority. It is a type of categorical data that simply allows us to classify or categorize. Examples include types of cuisine (Italian, Chinese, Mexican, etc.), hair color, or city of residence.

When two events are mutually exclusive, it means they cannot occur at the same time. Therefore, the probability that both will occur is 0.

The two main branches of statistics are descriptive and inferential. Descriptive statistics involves methods of organizing, picturing, and summarizing information from data. It provides simple summaries about the sample and measures, such as mean, median, mode, etc. Inferential statistics, on the other hand, involves methods of using information from a sample to draw conclusions (inferences) about the population. It includes various techniques like hypothesis testing, regression analysis, etc.

A sampling distribution is the distribution of a statistic (like a mean, median, or proportion) over many samples drawn from the same population. It is a distribution of all possible samples of the same size that can be obtained from a population.

The Kruskal-Wallis Test is used when the assumptions of the ANOVA (like normality, homogeneity of variances) are not met. It's a non-parametric alternative to the one-way ANOVA.

In a symmetric distribution, the skewness is zero. The distribution is neither left-skewed (negative skewness) nor right-skewed (positive skewness) but symmetric.

A T-test is typically used when the population variance is unknown. It's based on the t-distribution, which is a family of distributions that resemble the normal distribution but have heavier tails.

Factor loadings are the correlation coefficients between the factors and the variables. It is a measure of how much the variable is "explained" by the factor.

The variance of each Principal Component corresponds to the eigenvalues of the covariance matrix. A larger eigenvalue corresponds to a larger amount of variance explained by that Principal Component.