The distribution is unbalanced, because the right tail is larger than it would be if the distribution were balanced (symmetrical). Also called positive skew. See related link with diagrams that clarify this term.
i) Since Mean<Median the distribution is negatively skewed ii) Since Mean>Median the distribution is positively skewed iii) Median>Mode the distribution is positively skewed iv) Median<Mode the distribution is negatively skewed
When a distribution is skewed to the right, the mean is greater than median.
positively skewed
As the mean is greater than the median it will be positively skewed (skewed to the right), and if the median is larger than the mean it will be negatively skewed (skewed to the left)
Not necessarily.
A positively skewed or right skewed distribution means that the mean of the data falls to the right of the median. Picturewise, most of the frequency would occur to the left of the graph.
i) Since Mean<Median the distribution is negatively skewed ii) Since Mean>Median the distribution is positively skewed iii) Median>Mode the distribution is positively skewed iv) Median<Mode the distribution is negatively skewed
When a distribution is skewed to the right, the mean is greater than median.
In the majority of Empirical cases the mean will not be equal to the median, so the event is hardly unusual. If the mean is greater, then the distribution is poitivelt skewed (skewed to the right).
When the data distribution is negatively skewed.
The population data may be skewed and thus the mean is not a valid statistic. If mean > median, the data will be skewed to the right. If median > mean, the data is skewed to the left.
The distribution is skewed to the right.
Positively skewed.
positively skewed
As the mean is greater than the median it will be positively skewed (skewed to the right), and if the median is larger than the mean it will be negatively skewed (skewed to the left)
The mean may be a good measure but not if the data distribution is very skewed.
Not necessarily.