A good theory encompasses accuracy, consistency, broad scope, simplicity, and fruitfulness

From Beyond Falsifiability: Normal Science in a Multiverse by Sean M. Carrol.

What makes an explanation “the best.” Thomas Kuhn, after his influential book The Structure of Scientific Revolutions led many people to think of him as a relativist when it came to scientific claims, attempted to correct this misimpression by offering a list of criteria that scientists use in practice to judge one theory better than another one: accuracy, consistency, broad scope, simplicity, and fruitfulness. “Accuracy” (fitting the data) is one of these criteria, but by no means the sole one. Any working scientist can think of cases where each of these concepts has been invoked in favor of one theory or another. But there is no unambiguous algorithm according to which we can feed in these criteria, a list of theories, and a set of data, and expect the best theory to pop out. The way in which we judge scientific theories is inescapably reflective, messy, and human. That’s the reality of how science is actually done; it’s a matter of judgment, not of drawing bright lines between truth and falsity or science and non-science. Fortunately, in typical cases the accumulation of evidence eventually leaves only one viable theory in the eyes of most reasonable observers.