# Understanding Types Of Random Variables

Random variables are a type of statistical term that refers to a set of probabilities which depend on the past. These probabilities can be categorized into three main categories, namely, random events, random variables, and random combinations of these. In statistics and probability, a random variable, a non-convex random quantity, random number, or random occurrence, is described formally as a random event whose values vary according to certain previous results of a random process.

As an example, we may say that when you open the door to your house and find your dog on the floor due to being pushed or stepped upon by someone else, you are a victim of a random occurrence. On the other hand, if your dog is knocked down by a vehicle you are the victim of a random event. In the first case, you would experience a shock or pain that depends on the previous conditions. In the second case, the shock or pain you have experienced depends on the previous condition and the person who pushed or stepped upon your dog.

There are different types of random events that are based on probability. These include but are not limited to, individual random variables such as how people react to a stimulus, group random variables such as how a group reacts to a particular stimulus, and population random variables such as how a population reacts to a particular stimulus. We will now discuss random variables, in their various forms.

As previously mentioned, one type of random factors is the random event itself. This is the most commonly known random factor. This type of random variable is the one that is described in the first category, and it is the most commonly used. The random event itself can be considered as a mixture of a number of other random variables. These variables are randomly arranged within the event itself and depend upon the results of the past.

Random variables are a useful tool to use in analyzing the effect of an intervention on behavior, but they are often applied in situations where the intervention is not known in advance. This is where we use the second category of random factors.

The third category of random variables consists of random combinations of these other random variables. This category is applied more commonly and is used to describe a combination of two or more interventions. which can be used as a way to analyze the effects of a single intervention on behavior. For example, if you are a parent, and are deciding between ADHD treatments, you may consider whether your child is an ideal candidate for the drug Ritalin, but not for the diet and exercise program.

Random combination techniques are used to examine what happens when multiple interventions occur. The combinations can either reinforce each other or can counter them. As a case example, consider two people who have an addiction to drugs and alcohol. Both individuals are given the same intervention, which includes behavioral therapy, but one is given the intervention plus counseling and the other is given the intervention minus the intervention.

After three weeks, you can compare the differences in their behavior to see what has worked for one and what has not. If they were on the same course of treatment, then you would think that the one with the combined intervention would be the best and thus the best way to treat them. But, when the results are compared, you will find that the combined intervention was better. The fact that the combination is better means that it is the best overall choice.