Introduction:
In statistics, skewness is a measure of the asymmetry of a probability distribution. It indicates the extent to which the data is concentrated to one side of the mean compared to the other side. A positively skewed distribution has a long tail on the right side, while a negatively skewed distribution has a long tail on the left side.

Mean and Mode:
The mean is the average of a set of numbers, calculated by summing all the values and dividing by the total number of values. The mode, on the other hand, is the value that appears most frequently in a dataset. It represents the peak or highest point of the distribution.

Explanation:
When the mean is less than the mode, it indicates that the data is concentrated towards the higher values and there are a few extremely low values that pull the mean down. This situation leads to a negatively skewed distribution.

Example:
Let's consider an example to understand this concept better. Suppose we have a dataset of 10 numbers: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}. The mean of this dataset is (1+2+3+4+5+6+7+8+9+10)/10 = 5.5. However, the mode of this dataset is 1, as it appears most frequently.

If we plot a histogram of this dataset, we would observe that the data is concentrated towards the higher values (5, 6, 7, 8, 9, 10) and there is a long tail towards the lower values (1, 2, 3, 4). This distribution would be classified as negatively skewed.

Conclusion:
In conclusion, when the mean is less than the mode, it indicates a concentration of data towards higher values and a long tail towards lower values. This leads to a negatively skewed distribution. Therefore, the correct answer to the given question is option 'B' - Negatively skewed.