Final answer:
The choice between using the mean or median as a measure of center depends on the shape of the data and the presence of extreme outliers.
Step-by-step explanation:
In order to determine whether the mean or median provides a better description of the center of the distribution, we need to consider the shape of the data. If the data is symmetrical and does not have any extreme outliers, then the mean is usually a good measure of center. However, if the data is skewed or has extreme outliers, the median is a better measure of center.
Based on this understanding, we can classify each narrative:
A. Annual salary for corporate employees - Depends on the distribution of salaries. If there are extreme outliers (such as CEOs earning millions), then the median may be a better measure of center. If there are no extreme outliers, the mean can be a good measure of center.
B. Job satisfaction for corporate employees - Since job satisfaction is subjective and is not measured on a numerical scale, the median may be a better measure of center. This is because the distribution of job satisfaction may not follow a normal distribution.
C. Retirement age for corporate employees - This can vary greatly and may have extreme outliers (such as early retirements or very late retirements). In this case, the median may be a better measure of center.
D. Body temperature of incoming ER patients - Body temperatures are typically normally distributed and do not have extreme outliers. Therefore, the mean can be a good measure of center.
E. Blood pressure of incoming ER patients - Similar to body temperature, blood pressure is typically normally distributed and does not have extreme outliers. Therefore, the mean can be a good measure of center.
F. Waiting time of incoming ER patients - The waiting time can have a skewed distribution and may have extreme outliers. In this case, the median may be a better measure of center.