Shibata information criterion

The Shibata Information Criterion (SIC) is a way to measure how well a model or an equation fits or explains the data it is trying to explain. Basically, it tells us how well our equation or model matches the data we have. To understand it we can think of it like fitting a round peg into a round hole. If the peg fits perfectly, then we have a good match and the model explains the data very well. If the peg doesn't fit so perfectly, then the model doesn't explain the data as well. The SIC helps us to figure out how well our model fits and explains the data.