According to the textbook context, when precisely measured data are available, the simple correlation coefficient (Karl Pearson's coefficient) is more accurate than the rank correlation coefficient. The text explicitly states: 'However, generally it is not as accurate as the ordinary method. This is due the fact that all the information concerning the data is not utilised.' The rank correlation coefficient converts actual values into ranks, thereby losing precision. Since the simple correlation coefficient uses the actual numerical values of the data, it utilizes all available information and provides a more precise numerical value of the degree of linear relationship between two variables. The rank correlation coefficient is particularly useful when dealing with attributes that cannot be numerically measured or when data contains extreme values, but when precise measurements are available, the simple correlation coefficient is the more accurate choice.