In probability theory, the "complement" of an event A is the event that A does not occur, often denoted as A'. If the probability of event A happening is P(A), then the probability of it not happening, or its complement, is P(A') = 1 - P(A).

A Type I error occurs when the null hypothesis is true, but it is rejected. It is also known as a "false positive" result.

The power of a test is the probability that it correctly rejects a false null hypothesis, i.e., it is the probability of not making a Type II error.

If an outlier is added to a dataset, it can significantly increase the range, as the range is calculated as the difference between the maximum and minimum values in the dataset.

The Addition Rule is applicable when calculating the probability of the occurrence of at least one of two mutually exclusive events, while the Multiplication Rule is used to calculate the probability of two independent events both occurring.

In the field of statistics, a statistic is a numerical summary of a sample, as opposed to a population. It's a measure that is calculated from the sample data. For example, if we have data for a certain number of individuals from a larger group, the average of this data is a statistic.

In a Venn diagram, the intersection of events A and B is represented by the area where A and B overlap. This represents all outcomes that are in both A and B, or said another way, outcomes that satisfy both events A and B.

In a uniform distribution, all outcomes are equally likely. This is because the probability density function is constant within a certain range and zero elsewhere.

No, the Kruskal-Wallis Test does not require the data to be normally distributed. This is why it is classified as a non-parametric test.

Joint probability is a statistical term describing the likelihood of two events happening at the same time. It's the probability of the intersection of two or more events, often denoted as P(A ∩ B) for events A and B.