The coefficient in Spearman's Rank Correlation indicates the strength and direction of the association between two ranked variables. This coefficient can range from -1 (perfect negative correlation) to 1 (perfect positive correlation).

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.

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.

If the variance of a dataset is zero, then all the numbers in the set are the same. Variance measures how far a set of numbers is spread out from their average value. If all the numbers in the dataset are identical, there would be no dispersion and the variance would be zero.

A Spearman’s Rank Correlation coefficient of 0 indicates that there is no correlation, meaning changes in one variable do not correspond to changes in the other variable.