The article discusses how measurement errors in data can affect the results of statistical analyses. The researchers suggest a new model where respondents provide their best estimates based on the information they have, leading to errors that are not related to the reported values but are correlated with the true values. This can cause both over- and under-estimation of results in linear regression. The study also shows that using instrumental variables methods to correct for measurement errors may introduce bias. The calculations indicate that the range of estimates for the impact of education on outcomes can vary widely due to measurement errors.