Random variables refer to random values, as well as capital letters, such as Z or X, in relation to the numerical values that they could take by using the lowercase letters x and y. Table 4.1 “4 Random Variables” provides four examples of randomly-occurring values. The first value in the list, N (which represents the total number of observations) is the most obvious, yet the second value, x, is less obvious. It would be wrong to say that the values x and y are random, since they represent two distinct elements of the set of all observations in the data set. In this example, we are looking at N x (N y), or more technically, the value of x is equal to the total value of N, or N x minus N y, or N x minus x, or N x minus y minus x.
Random variables are a crucial part of the scientific method. Most of the data that we observe, both in the laboratory and in our everyday lives, can be considered random or at least can be considered consistent with randomness. When there are no specific characteristics of the data or experiment, then it is possible to say that the data is random. Unfortunately, most experiments fail miserably because the data that we see is not truly random. If, for example, you were to ask a random sample of people to give you their opinions on various products, or to rate the beauty of different women, you would probably see that the vast majority of people are biased and will provide results that are different from the true result.
This phenomenon occurs because the experiment may require participants to perform an action (giving you their opinion about a product) that they would normally have performed without the experiment (rating the beauty of a woman). The participants then will give results that are not true to what they would normally give, or would have given if the experiment was run in the other way, because they were asked to perform an action without performing the experiment.
Table 4.2 (3) “4) “4 Random Variables: 4 Examples of Random Variables,” also known as Table 4. Contains a different example of a statistic with a random value added. We are looking at the value of a random number generator, which can be seen in the equation: N(x) = N(y) + x. This means that the value of x, the number of observations, is equal to the total value of N, or N x – N y, or N x minus N y – N x, or N x minus N x -x minus y minus x.
The second example is “The second random variable in this example.” This involves it being the difference between x minus x. The difference between the numbers x and y is equal to x minus the difference between the values of x and y minus x.
In order for the second example to work, it would have to be assumed that the difference between the numbers is a completely random occurrence. If it were not, then the second example would be irrelevant because it would simply be a random number.
So, if you are asking how to get students to answer a university exam, it would help to know that you would be able to find a “true random number” or a “pseudo-random number”. The difference between those two random numbers would be that pseudo random numbers have no outside influence on their value, while true random numbers are actually true random and do not depend on the outside influences.