The single index model is a flexible version of linear regression that doesn't need specific assumptions about errors. Score tests were developed to check for errors like heteroscedasticity, autocorrelation, or missing variables in the model. These tests were found to be effective in identifying model mistakes and outperformed other methods in simulations. Real data analysis showed that these tests can catch important errors that affect the results, highlighting the importance of checking for model misspecifications.