The range of a dataset is sensitive to outliers. Because the range is calculated as the difference between the maximum and minimum values, an outlier (an extremely high or low value) can greatly increase the range.

The type of factor analysis in which the researcher assumes that all variance in the observed variables is common variance is known as common factor analysis.

The Kruskal-Wallis Test is used to compare three or more independent samples. It's an extension of the Mann-Whitney U Test for more than two groups.

Spearman's Rank Correlation is preferable to Pearson's correlation when the relationship between variables is non-linear but monotonic. Pearson's correlation measures linear relationships, while Spearman's can capture non-linear relationships.

The z-value associated with a 95% confidence interval in a standard normal distribution is approximately 1.96. This means that we are 95% confident that the true population parameter lies within 1.96 standard deviations of the sample mean.

The interquartile range, which is the difference between the upper quartile (Q3) and the lower quartile (Q1), represents the middle 50% of the data and is not affected by outliers. The range, on the other hand, is the difference between the maximum and minimum data values and is significantly affected by outliers.

The main purpose of simple linear regression is to understand the relationship between two variables. It provides a quantitative estimate of the relationship between one dependent variable and one independent variable.

The coefficient of variation (CV) is a standardized measure of dispersion that is unitless. It's particularly useful when comparing the dispersion of two or more datasets that have different units or significantly different means. Standard deviation, on the other hand, has the same units as the data, which may not be helpful for comparisons across different datasets.

The conditional probability of an event A given an event B equals the marginal probability of A when A and B are independent. This is because the occurrence of B does not change the probability of A if they are independent.

The Mann-Whitney U test is used for ordinal data, which can be ranked but have unknown or non-equivalent differences between values. It can also be used with interval and ratio data that do not meet the assumptions of other tests.