Specifically, the significance depends mostly on the sample size. As explained before, in very large samples, even very small relations between variables will be significant, whereas in very small samples even very large relations cannot be considered reliable (significant).
Thus, in order to determine the level of statistical significance, we need a function that represents the relationship between "magnitude" and "significance" of relations between two variables, depending on the sample size.

The function we need would tell us exactly "how likely it is to obtain a relation of a given magnitude (or larger) from a sample of a given size, assuming that there is no such relation between those variables in the population."
In other words, that function would give us the significance (p) level, and it would tell us the probability of error involved in rejecting the idea that the relation in question does not exist in the population.

This 'other" hypothesis, that is, the one that states that there is no relation in the population, is usually called the null hypothesis, and we mentioned this earlier when looking at how a research question could be re-formed into a research hypothesis.
So a key principle to understand is that generally at least, we are TESTING the NULL HYPOTHESIS against an ALTERNATIVE HYPOTHESIS according to an agreed or set LEVEL of SIGNIFICANCE.

It would be ideal if the probability function was linear and, for example, only had different slopes for different sample sizes. Unfortunately, the function is more complex and is not always exactly the same; however, in most cases we know its shape and can use it to determine the significance levels for our findings in samples of a particular size. Most of these functions are related to a general type of function, which is called the normal distribution and at the level of university statistics that we are working, this is a very important statistical concept to understand.