In a two-way ANOVA, interaction effects show how the levels of one independent variable affect the effect of the other variable on the dependent variable. Essentially, it shows whether the effect of one independent variable depends on the level of the other independent variable.

The Poisson distribution is used for describing the distribution of rare events in a large population or time/space interval. It also describes events that are independent, meaning the occurrence of one event doesn't affect the occurrence of another.

In a positively skewed distribution, the mean is usually greater than the median. A positive skew means the right tail of the distribution is longer or fatter. The mean, being affected by the values of the data points, gets dragged in the direction of the tail, and hence is typically greater than the median in a positively skewed distribution.

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.

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.