If the results of an ANOVA test are significant, post-hoc tests are often used to identify specifically which groups' means are different. These tests are performed after the ANOVA and help to avoid type I errors when making multiple comparisons.

The primary objective of cluster analysis is to group similar instances (observations, data points, etc.) into clusters.

If the F statistic in an ANOVA is significantly high, it suggests that the null hypothesis should be rejected. The null hypothesis in ANOVA is typically that all group means are equal.

If the population standard deviation is unknown, the sample standard deviation is used to estimate the standard error of the mean. The standard error is a measure of how much the sample mean is expected to vary from the true population mean.

Pearson's correlation coefficient (denoted as r) is a measure of the strength and direction of association that exists between two continuous variables. It measures the degree to which pairs of data for these two variables lie on a line. The values lie between -1 and 1, where 1 indicates a perfect positive correlation, -1 a perfect negative correlation, and 0 no correlation at all.

Quantitative data represents quantities and can be measured on a Numerical scale. It includes both discrete data (e.g., the number of students in a class) and continuous data (e.g., the weight of a person).

The standard deviation and mean absolute deviation both measure the dispersion in a dataset. The key difference lies in how they treat deviations from the mean: standard deviation squares the deviations before averaging them, while mean absolute deviation takes the absolute value of deviations before averaging. As a result, standard deviation is more sensitive to extreme values than the mean absolute deviation.

Even in the presence of multicollinearity, the least squares estimates of the regression coefficients are still unbiased. However, they are less precise and have high standard errors.

Multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. This can lead to unstable estimates of the coefficients.

The result of a Kruskal-Wallis Test is interpreted as a measure of difference between groups. If the test is significant, it suggests that at least one of the groups differs from the others.

The geometric mean is particularly useful when comparing different items with very different ranges. It is used in various kinds of growth rates, like population growth or financial growth, where each year's value is relative to the previous year's value.

A uniform distribution, also called a rectangular distribution, is a type of probability distribution in which all outcomes are equally likely. Each interval of equal length on the distribution's support has the same probability.