You've over simplified and created a major error. It's a common one though, so allow me to explain.
There is no single entity Google cares about. There is no "average user" (nor median). Trying to make the product best for an "average" user makes it bad for everyone. You have made the assumption that this solution space is smooth and relatively uniformly distributed.
Consider this: on a uniformly distribution, if you interpolate between two points, those interpolations are representative of the distribution. But if you do so on a gaussian distribution, this is not true. This is because the density of these distributions are not evenly distributed (this is what "uniform" in uniform distribution means). For more complicated distributions you need different interpolations. Many real world distributions are the agglomeration of varying distributions (you probably have clusters and pockets).
The distribution of searches (or customers) is neither normal nor uniform.
To understand some further complexity, it is important to remember that the burden of success is the requirement for further nuance. Pareto. It may take few resources to get to "80%"/"good enough" but significant to increase that another 10%. Think about a Taylor Series (Fourier Approximation, or pick your preferred example), the first order approximation will only take you so far. It may be good enough to start, but the need for higher order (and this more complex calculations) approximations are increasingly necessary for precision (non-linearly). So simplify where you can, but don't forget what you've done. If you do, you'll be stuck. Or worse, you will compound your error (also very common).
