The choice of visualization tool varies depending on the problem being addressed and the specific needs of each individual project. If the project involves measuring a process that includes a small sample size of continuous data types for the purpose of displaying the frequency of the distribution of data and estimate where most of the values tend to occur, and data patterns (shape), then a histogram would be the visualization tool of choice.
Central tendency can be estimated using the graphical display of the histogram, which provides a quick summary of lots of data. The histogram reveals the central position of the data, the most frequently occurring value, the spread or variation of the data, shape and range of the numeric of the data. Another advantage of the histogram is to show the variation in the data and we can readily identify extreme data points. The shape of the histogram could also show us if the process that we are trying to measure is stable and there are no outliers present (symmetric bell-shaped). Histogram could also show if our process data reveals two segments (bi-modal) or process data that may not be normally distributed (positively or negatively skewed/asymmetric) that would warrant further investigation and use of the next level of visualization tool and statistical analysis to draw meaningful conclusions with greater level of certainty.
Although the histogram helps us quickly analyze the data graphically and provide estimates on central tendency, it does not provide a definitive indication of a normal distribution but can be more precisely measured by statistical measures of central tendency, namely the mean, median and the mode. While the central tendency and dispersion graphical analysis provide valuable information, they are not sufficient in describing variation that could be accomplished by checking normality of data and how the data departs significantly from the normal distribution thru advanced statistical technique.