Suppose a quality assurance team gathers a preliminary sample of five runs of an experiment and finds a sample mean of 10.25 with a standard deviation of 1.25. They want to know whether the population mean is less than 11. Select the most appropriate interpretation of Type I Error and Type II Error.

A Type I Error corresponds to concluding the population mean is greater than or equal to 11, when in reality it is less than 11. A Type II Error corresponds to concluding the population mean is less than 11, when in reality it is greater than 11.

A Type I Error corresponds to concluding the population mean is less than 11, when in reality it is greater than or equal to 11. A Type II Error corresponds to concluding the population mean is greater than or equal to 11, when in reality it is less than 11.

A Type I Error corresponds to concluding the population mean is greater than 11, when in reality it is less than or equal to 11. A Type II Error corresponds to concluding the population mean is greater than or equal to 11, when in reality it is equal to 11.

A Type I Error corresponds to concluding the population mean is greater than 11, when in reality it is less than 11. A Type II Error corresponds to concluding the population mean is greater than or equal to 11, when in reality it is less than 11.