Right-Skewed Distribution vs Left-Skewed Distribution
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Right-skewed distribution
Describing data
A right-skewed distribution has its long tail stretching toward the high values, which typically pulls the mean above the median.
Skew is named for the tail, not for the peak, so a right-skewed graph piles up on the left and trails off to the right. The few large values out in that tail drag the mean upward while barely moving the median, so the mean typically lands above the median. For the values 1, 2, 2, 3, 22 the mean is while the median is 2. Treat that ordering as a strong tendency rather than a theorem, since it is possible to build a right-skewed data set whose mean is not above its median.
Left-skewed distribution
Describing data
A left-skewed distribution has its long tail stretching toward the low values, which typically pulls the mean below the median.
A left-skewed graph piles up on the right and trails off to the left, because the tail is what names the skew. Those few small values in the tail pull the mean down while the median stays put, so the mean typically comes in below the median. For the values 2, 18, 19, 20, 21 the mean is while the median is 19. Scores on an easy test often look this way, with most students bunched near the top and a handful far below, but treat the mean-below-median pattern as a tendency rather than a rule.