Final answer:
Joana is not necessarily correct; the mean is not always the best measure of center for a distribution. The median is usually better when dealing with outliers, while the choice between mean, median, and mode depends on the data distribution's characteristics.
Step-by-step explanation:
Joana believes that the mean is the best measure of center to describe a distribution, but this is not always correct. The mean, or average, is a good measure when the data set has no extreme values or outliers. However, the median is often a better measure of center when a data set includes outliers, as it is not influenced by the extreme values.
The mean is easy to calculate and useful for comparisons, but it can be skewed by outliers. The median is the middle value when data is ordered, and the mode is the most frequently occurring value, which may also be relevant in certain cases. To determine the most appropriate measure of center, one has to examine the shape of the data distribution and consider the presence of outliers.
For distributions with a symmetric shape and no outliers, the mean may provide the best measure of center. However, for skewed distributions or those with outliers, the median or sometimes mode could be more representative. Therefore, the choice between mean, median, and mode should be based on the specifics of the data set.