Answer:
Here are two example data sets that meet the given conditions:
Data Set 1:
Values: 10, 12, 14, 16, 18, 20
Mean: (10+12+14+16+18+20) / 6 = 15
MAD: Mean Absolute Deviation = sum(abs(x - mean))/n = ((|10-15| + |12-15| + |14-15| + |16-15| + |18-15| + |20-15|)/6) = 2.5
Story: This dataset may have originated from measuring the time taken to complete a series of tasks by a group of skilled workers. The values represent the time taken in seconds, and the low MAD value indicates that the workers' performance was consistent. The mean value of 15 seconds suggests that the workers were efficient, completing the tasks quickly and accurately.
Data Set 2:
Values: 3, 5, 7, 9, 11, 13
Mean: (3+5+7+9+11+13) / 6 = 8
MAD: Mean Absolute Deviation = sum(abs(x - mean))/n = ((|3-8| + |5-8| + |7-8| + |9-8| + |11-8| + |13-8|)/6) = 2.5
Story: This dataset may have originated from measuring the height of a group of plants grown under different conditions. The values represent the height in inches, and the low MAD value indicates that the plants' growth was consistent across all conditions. The mean value of 8 inches suggests that the conditions were not optimal for plant growth, as the mean height is lower than the expected average for this type of plant.
Both data sets have a similar MAD value of 2.5, indicating that the variation in the data is relatively low, and the differences in the mean values suggest that they came from different sources. In both cases, the means are more than one MAD apart, indicating that there are significant differences between the values in each set.