The article explores how different types of errors can affect the results of certain statistical models. By looking at Probit and Tobit models, the researchers found that not accounting for these errors can lead to inaccurate estimates of effects. They developed new methods to calculate more reliable estimates and confidence intervals. These methods were tested in simulations and real-world applications, showing their effectiveness in handling errors in the data.