Discrete data: Graphs and skewness I am studying an introductory course in statistics, Essentials of Statistics.
The author mentioned that histograms are used to represent the frequency distribution of a continuous data. Then directly, he explained how to detect if there is skewness in the data using histograms. Later he highlighted some other types of plots and graphs including the bar chart.
What is missing for me: What are the graphs that represent quantitative discrete data?
If using a bar chart, then is it possible to use bar chart to detect the skewness in a quantitative discrete data?
 A: You can represent univariate discrete data well using a bar plot, where the value of the variable is on the horizontal axis and the frequency/proportion of outcomes is on the vertical axis.  This type of plot is essentially a type of histogram for discrete data.$^\dagger$  As for diagnosing skewness in the data, this should be reasonably evident from visual inspection of the bar plot in most cases, but it might be hard to diagnose in some difficult cases.  You can supplement a visual assessment of skewness by computing the sample skewness for the data as one of your descriptive statistics.

$^\dagger$ Technically speaking, a bar plot for a univariate discrete variable (taking on integer vaules) is a histogram that using "bins" that each contain an individual discrete outcome (i.e., a single integer), with the axis for the histogram taken only over discrete outcomes rather than a continuum.
A: Does the author specify which definition of skewness is being used? There are several and normally do not rely on looking at histograms.
I am guessing that the author is proposing a (vague) definition of skewness that you could apply to discrete quantitative variables. You could plot an histogram for this and proceed as if you were dealing with a continuous variable.
A: You also use a histogram to represent discrete data, including quantities like counts. A histogram is a visual representation of the frequency or probability by area over values of $x$ (for values of a discrete variable) or probability density—probability of $x$ for a given range of specific values of $x$ (i.e. for values of a continuous variable).
A bar chart, on the other hand, is a graph relating two different variables (e.g. $x$ is number of oranges eaten per capita per year, and $y$ is country). So you do not really use a bar chart to represent the distribution of a single variable.
