Shape of a Distribution vs Skewness
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.
Shape of a distribution
Describing data
The shape of a distribution is its symmetry or skew together with its number of peaks, read directly off a histogram, dotplot, or stemplot.
Shape is what you can see before computing anything: whether the graph is roughly symmetric or has a longer tail on one side, and whether it has one peak, two peaks, or none. You name the skew after the side the longer tail points to, so a stretched-out right tail means skewed right. A histogram of home sale prices in one town usually climbs fast, peaks near the low end, and trails off toward a few expensive houses, which you would describe as unimodal and skewed right. Shape then drives the rest of your description, because it decides whether you summarize with the mean and standard deviation or with the median and IQR.
Skewness
Describing data
Skewness describes the asymmetry of a distribution, meaning that one tail is longer or stretched further than the other.
A distribution is skewed when values trail off farther on one side than the other. In a right-skewed distribution the long tail points toward high values, which pulls the mean above the median; in a left-skewed distribution the reverse happens. For example, incomes are often right-skewed because a few very high earners stretch the upper tail. A useful rule of thumb is that the mean gets dragged toward the longer tail.