The chapter discusses the scaling and assessment of data quality in crystallography, focusing on the physical factors affecting measured diffraction intensities and the scaling models used to standardize these intensities. The scaling process involves correcting for various experimental factors such as the primary beam, crystal rotation, diffracted beam direction, and detector characteristics. The scaling model is chosen to reflect the specific diffraction experiment and can include correction factors for incident beam intensity variations, radiation damage, and detector distortions. The scaling results in a more consistent dataset, which can then be analyzed to determine the real resolution, identify bad regions, assess radiation damage, and evaluate overall data quality. The chapter also covers the use of probability and correlation analysis to assess the significance of anomalous signals and the algorithms used in the *CCP4* program *SCALA* for scaling and merging intensities. Additionally, it introduces the new program *POINTLESS*, which is designed to determine the Laue group and space group of a crystal from the diffraction data, aiding in the initial examination and data collection strategy. The assessment of data quality involves evaluating the internal consistency of the data through metrics such as $R$ factors and correlation coefficients. The chapter provides methods for detecting and rejecting outliers, determining the real resolution, and identifying bad regions in the data. It also discusses the scaling of multiple-wavelength data sets and the detection of anomalous signals, emphasizing the importance of these steps in structure determination.The chapter discusses the scaling and assessment of data quality in crystallography, focusing on the physical factors affecting measured diffraction intensities and the scaling models used to standardize these intensities. The scaling process involves correcting for various experimental factors such as the primary beam, crystal rotation, diffracted beam direction, and detector characteristics. The scaling model is chosen to reflect the specific diffraction experiment and can include correction factors for incident beam intensity variations, radiation damage, and detector distortions. The scaling results in a more consistent dataset, which can then be analyzed to determine the real resolution, identify bad regions, assess radiation damage, and evaluate overall data quality. The chapter also covers the use of probability and correlation analysis to assess the significance of anomalous signals and the algorithms used in the *CCP4* program *SCALA* for scaling and merging intensities. Additionally, it introduces the new program *POINTLESS*, which is designed to determine the Laue group and space group of a crystal from the diffraction data, aiding in the initial examination and data collection strategy. The assessment of data quality involves evaluating the internal consistency of the data through metrics such as $R$ factors and correlation coefficients. The chapter provides methods for detecting and rejecting outliers, determining the real resolution, and identifying bad regions in the data. It also discusses the scaling of multiple-wavelength data sets and the detection of anomalous signals, emphasizing the importance of these steps in structure determination.