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At the very foundation of studying random events and the likelihood of various outcomes is probability theory. It's a branch of mathematics that deals with the analysis of random phenomena. The core components of probability theory include experiments, sample spaces, events, and probabilities associated with these events.

An experiment refers to a procedure that yields one of several possible outcomes. The set of all possible outcomes is called the sample space. An event is a subset of the sample space to which a probability is assigned. If you flip a coin, the sample space is {Heads, Tails}, and each of these outcomes is an event. Probability theory provides the tools to calculate how likely these events are to occur, offering a precise way to quantify uncertainty.

In studying probability, a random variable serves as a numerical description of the outcomes of a statistical experiment. A random variable, often denoted by letters such as X, Y, or Z, can take on different values, with each value having an associated probability.

Random variables come in two types: discrete and continuous. Discrete random variables have specific, separate values (like the number of books on a shelf), while continuous random variables represent measurements and can take on any value within a range (such as the length of a book). Understanding random variables is key to grasping concepts like the expectation value, as they describe the possible outcomes upon which probabilities are assessed.

Building upon the concept of random variables, a probability distribution is a mathematical function that describes how probabilities are distributed over the values of a random variable. It is essentially a collection of all possible values of the random variable and the corresponding probabilities that these values will occur.

There are many types of probability distributions, each serving different types of data and experiments. Common distributions include the uniform distribution (where all outcomes are equally likely), binomial distribution (dealing with a fixed number of binary outcomes), normal distribution (a continuous distribution that depicts many natural phenomena), and others. Distributions are tools for predicting and understanding various patterns and trends in the realm of probabilities.

The beauty of probability theory emerges in its mathematical representation. Each concept, from random variables to distributions, is described using precise mathematical equations or functions.

For discrete random variables, the expectation value is calculated as a weighted average, where each value of the variable is multiplied by its probability and then summed up. In contrast, for continuous variables, integrals take over the summation process, reflecting the necessity to account for values over a continuous range. This mathematical representation is powerful as it allows one to perform precise calculations, derive further statistical properties, and visually represent data through graphs and functions. Understanding the math behind these concepts is crucial in order to correctly assess and interpret probabilistic information.

Central tendency is all about the center or the 'average' of a dataset or a probability distribution. The most common measures of central tendency include the mean, median, and mode. In probability, the mean, often referred to as the expectation value, is the theoretical average of all possible outcomes of a random variable, considering their probabilities.

This measure is incredibly useful as it provides a single value that summarizes the overall behavior of a data set or random process. The central tendency can tell us what value is typical or expected in a particular context, making it an indispensable tool in a wide array of fields, from economics to engineering.