1. Magnitude or "size":
    The magnitude is much easier to understand and measure than the reliability.
    For example and referring to the example we have used about gender and white cell count: if every male in our sample was found to have a higher WCC than any female in the sample, we could say that the magnitude of the relation between the two variables (Gender and WCC) is very high in our sample. In other words, we could predict one based on the other (at least among the members of our sample).
  2. Reliability or "truthfulness":
    The reliability of a relation is a much less intuitive concept, but still extremely important.
    It is concerned with the "representativeness" of the result found in our specific sample for the entire population.
    In other words, it says how probable it is that a similar relation would be found if the experiment was replicated, that is, repeated with other samples drawn from the same population.
    Remember that we are almost never "ultimately" interested only in what is going on in our sample; we are interested in the sample only to the extent it can provide information about the population. If our study meets some specific criteria (to be mentioned later), then the reliability of a relation between variables observed in our sample can be quantitatively estimated and represented using a standard measure (technically called p-value or statistical significance level, which is discussed later).