The article by P. Andrew Karplus and Kay Diederichs discusses the challenges in determining the high-resolution limit for crystallographic data and the limitations of current standard protocols. They introduce a new statistic, CC*, which estimates the correlation between observed data and the underlying true signal. This statistic provides a single, statistically valid guide for assessing the quality of both the model and the data, allowing for a more informed decision on the high-resolution cutoff. The authors demonstrate that data out to resolutions beyond the typical cutoff criteria can improve model quality, contrary to current practices. They also show that the Pearson correlation coefficient (CC) can be used to assess both data accuracy and the agreement between the model and the data on the same scale. The analytical relationship between CC1/2 and CC* is derived, and it is validated through simulations, highlighting the robustness and utility of CC* in crystallography and potentially other scientific fields involving multiply measured data.The article by P. Andrew Karplus and Kay Diederichs discusses the challenges in determining the high-resolution limit for crystallographic data and the limitations of current standard protocols. They introduce a new statistic, CC*, which estimates the correlation between observed data and the underlying true signal. This statistic provides a single, statistically valid guide for assessing the quality of both the model and the data, allowing for a more informed decision on the high-resolution cutoff. The authors demonstrate that data out to resolutions beyond the typical cutoff criteria can improve model quality, contrary to current practices. They also show that the Pearson correlation coefficient (CC) can be used to assess both data accuracy and the agreement between the model and the data on the same scale. The analytical relationship between CC1/2 and CC* is derived, and it is validated through simulations, highlighting the robustness and utility of CC* in crystallography and potentially other scientific fields involving multiply measured data.