In simple terms, statistical modeling is a simplified, mathematically-formalized way to approximate reality (i.e. what generates your data) and optionally to make predictions from this approximation. The statistical model is the mathematical equation that is used. Here is a basic example. Suppose you want to report the weight of a variety of potatoes. We will consider a hard and an easy way to do it. The hard way is spending years measuring the weight of every single potato of this variety in the world, and reporting your data in an endless Excel spreadsheet. The easy way is selecting a 30 potato-wide representative sample of this variety, computing its average and standard deviation and reporting only those two numbers as an approximate description of this weight. Representing a quantity by an average and a standard deviation is a very simple form of statistical modeling. Another example is attempting to represent the height of plants according to soil water content by a straight line characterized by a slope and an intercept drawn after an experiment on a sample of plants submitted to a growing soil humidity. This particular model is called simple linear regression.