Here, a model is selected over another plausible one. The accuracy of one model seems higher than the other. The area under curve (AUC) of the ROC of a model is greater than that of another. However, it is not appropriate to base the conclusion on pure numbers only. It is important to conclude whether the numbers hold significance from the point of view of statistical inference. In the analytical world, it is pivotal that we make use of statistical tests whenever they are available to validate claims/hypotheses. A reason for using statistical tests is that probability can be highly counterintuitive, and what appears on the surface might not be the case upon closer inspection, after incorporating the chance variation. For instance, if a fair coin is tossed 100 times, it is imprudent to think that the number of heads must be exactly 50. Hence, if a fair coin shows up 45 heads, we need to incorporate the chance variation that the number of heads can be less than 50 too. Caution must be exerted all the while when we are dealing with uncertain data. A few examples are in order here. Two variables might appear to be independent of each other, and the correlation might also be nearly equal to zero. However, applying the correlation test might result in the conclusion that the correlation is not significantly zero. Since we will be sampling and resampling a lot in this text, we will look at related tests.