The article discusses the Rietveld refinement method and the interpretation of R factors and other error indices used in diffraction analysis. It explains that while smaller R factors indicate a better fit of a model to the data, they may not always reflect the quality of the model, especially if the data are of poor quality. The article defines and discusses key Rietveld error indices, such as the weighted profile R factor (Rwp), chi-squared (χ²), and the expected R factor (Rexp). It emphasizes that these indices are not sufficient on their own to judge the quality of a refinement, as they do not account for the chemical reasonableness of the model. Graphical analysis of the fit is also important. The article explains that the Rietveld algorithm minimizes the weighted sum of squared differences between observed and computed intensities. It discusses the concept of expected R factor (Rexp), which represents the best possible value for a given set of data if the model is correct. It also discusses the relationship between χ² and Rwp, and notes that χ² should never drop below one, as this would indicate that the model is not correctly accounting for the data uncertainties. The article also discusses the reflection-based R factor (Rf) and its use in comparing Rietveld results with single-crystal diffraction results. It notes that Rf values can be used to assess the accuracy of the model in reproducing the crystallographic observations. However, it also warns that Rf values can be misleading if the model is not chemically plausible or if the data contain significant background or systematic errors. The article concludes that while R factors and other error indices are useful in assessing the quality of a Rietveld refinement, they should not be used in isolation. The chemical reasonableness of the model and graphical analysis of the fit are also important in determining the quality of the refinement. The article also notes that the Rietveld method is sensitive to the quality of the data and that improvements in data quality can lead to better refinements, even if the discrepancy indices appear to worsen.The article discusses the Rietveld refinement method and the interpretation of R factors and other error indices used in diffraction analysis. It explains that while smaller R factors indicate a better fit of a model to the data, they may not always reflect the quality of the model, especially if the data are of poor quality. The article defines and discusses key Rietveld error indices, such as the weighted profile R factor (Rwp), chi-squared (χ²), and the expected R factor (Rexp). It emphasizes that these indices are not sufficient on their own to judge the quality of a refinement, as they do not account for the chemical reasonableness of the model. Graphical analysis of the fit is also important. The article explains that the Rietveld algorithm minimizes the weighted sum of squared differences between observed and computed intensities. It discusses the concept of expected R factor (Rexp), which represents the best possible value for a given set of data if the model is correct. It also discusses the relationship between χ² and Rwp, and notes that χ² should never drop below one, as this would indicate that the model is not correctly accounting for the data uncertainties. The article also discusses the reflection-based R factor (Rf) and its use in comparing Rietveld results with single-crystal diffraction results. It notes that Rf values can be used to assess the accuracy of the model in reproducing the crystallographic observations. However, it also warns that Rf values can be misleading if the model is not chemically plausible or if the data contain significant background or systematic errors. The article concludes that while R factors and other error indices are useful in assessing the quality of a Rietveld refinement, they should not be used in isolation. The chemical reasonableness of the model and graphical analysis of the fit are also important in determining the quality of the refinement. The article also notes that the Rietveld method is sensitive to the quality of the data and that improvements in data quality can lead to better refinements, even if the discrepancy indices appear to worsen.