Descriptive statistics summarizes and interprets the characteristics of a dataset. These characteristics can include measures of central tendency like mean, median, and mode, measures of dispersion like range, variance, and standard deviation, and measures of shape like skewness and kurtosis. This branch of statistics provides a summary about the samples and the measures that have been made. It's essentially a way to describe and summarize the data.

Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of a given distribution contain extreme values. Positive kurtosis indicates a distribution with tails or outliers that are fatter and more extreme than a normal distribution.

Bayes' theorem can be applied to hypothesis testing by calculating the probability of the hypothesis given the observed data. This differs from traditional frequentist hypothesis testing, where the data is assumed given and the hypothesis is tested.

If the p-value in a Chi-square test is less than the chosen significance level, we reject the null hypothesis. This means that we have enough evidence to conclude that the two variables are not independent.

Non-parametric tests are used when the data doesn't meet the assumptions of parametric tests like the t-test, such as when the data is not normally distributed.

The F-test in multiple linear regression is used to test the overall significance of the model, essentially testing whether at least one of the predictors' coefficients is non-zero and hence contributes to explaining the variability in the response variable.

Bayesian methods, which are grounded in Bayes' theorem, play an integral role in many areas of machine learning. They allow the model to update its predictions as it receives more data, making them particularly useful for tasks involving prediction and recommendation.

Multicollinearity can be addressed by removing one or more of the highly correlated independent variables.

Multicollinearity refers to the situation in multiple linear regression where the predictor variables are highly correlated. This can lead to unstable estimates of the coefficients which can change erratically in response to small changes in the model.

Categorical data is best suited for a Chi-square test. The Chi-square test is used to determine if there is a significant association between two categorical variables.

In an ANOVA, the F statistic is the ratio of the between-group variance to the within-group variance. It represents the extent to which group means differ from each other, compared to the variability within groups.

The probability of the intersection of Events A and B is represented by P(A ∩ B), which means the probability that both events A and B occur.