The probability of an event A, given that another event B has occurred, is called the conditional probability of A given B. It is denoted as P(A

The sum of the squared loadings for a factor (i.e., the column in the factor matrix) which represents the variance in all the variables accounted for by the factor is known as eigenvalue in factor analysis.

When the residuals exhibit a pattern or trend rather than a random scatter, it can be a sign of model misspecification, i.e., the model doesn't properly capture the relationship between the predictors and the outcome variable.

Inferential statistics is the branch of statistics that involves using a sample to draw conclusions about a population. It takes data from a sample and makes inferences about the larger population from which the sample was drawn. For example, inferential statistics might use data from a sample of women to infer something about the mean weight of all women.

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Its primary purpose is to identify the underlying structure and relationships within a set of variables.

A suitable data transformation may make it possible to use a parametric test instead of a non-parametric test. Transformations can help to stabilize variances, normalize the data, or linearize relationships between variables, allowing for the use of parametric tests that might have more statistical power.

If two events are independent, the conditional probability of one event given the other is simply the probability of the event itself. This is because in independent events, the occurrence of one event does not affect the occurrence of the other event.

A Type II error, also known as a false negative, occurs when the null hypothesis is false, but we fail to reject it.

The mode can be misleading or irrelevant especially with continuous data. Since the mode is the most frequently occurring value, with continuous data the frequency of each value is often the same (i.e., 1), hence it becomes difficult to define a mode in a traditional sense.

A VIF greater than 5 is often taken as an indication of high multicollinearity among the predictors in a regression model. This could lead to imprecise and unreliable estimates of the regression coefficients.