## Aggregation in Graphical Representation: 10 Quotes

*“Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers even a very large set — is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful.” (Edward R Tufte, “The Visual Display of Quantitative Information”, 1983)*

*“A good description of the data summarizes the systematic variation and leaves residuals that look structureless. That is, the residuals exhibit no patterns and have no exceptionally large values, or outliers. Any structure present in the residuals indicates an inadequate fit. Looking at the residuals laid out in an overlay helps to spot patterns and outliers and to associate them with their source in the data.” (Christopher H Schrnid, “Value Splitting: Taking the Data Apart”, 1991)*

“Without meaningful data there can be no meaningful analysis. The interpretation of any data set must be based upon the context of those data. Unfortunately, much of the data reported to executives today are aggregated and summed over so many different operating units and processes that they cannot be said to have any context except a historical one — they were all collected during the same time period. While this may be rational with monetary figures, it can be devastating to other types of data.” *(Donald J Wheeler, “Understanding Variation: The Key to Managing Chaos” 2nd Ed., 2000)*

*“Graphical displays are often constructed to place principal focus on the individual observations in a dataset, and this is particularly helpful in identifying both the typical positions of data points and unusual or influential cases. However, in many investigations, principal interest lies in identifying the nature of underlying trends and relationships between variables, and so it is often helpful to enhance graphical displays in ways which give deeper insight into these features. This can be very beneficial both for small datasets, where variation can obscure underlying patterns, and large datasets, where the volume of data is so large that effective representation inevitably involves suitable summaries.” (Adrian W Bowman, “Smoothing Techniques for Visualisation” [in “Handbook of Data Visualization”], 2008)*

*“In order to be effective a descriptive statistic has to make sense — it has to distill some essential characteristic of the data into a value that is both appropriate and understandable. […] the justification for computing any given statistic must come from the nature of the data themselves — it cannot come from the arithmetic, nor can it come from the statistic. If the data are a meaningless collection of values, then the summary statistics will also be meaningless — no arithmetic operation can magically create meaning out of nonsense. Therefore, the meaning of any statistic has to come from the context for the data, while the appropriateness of any statistic will depend upon the use we intend to make of that statistic.” (Donald J Wheeler, “Myths About Data Analysis”, International Lean & Six Sigma Conference, 2012)*

*“A common mistake is that all visualization must be simple, but this skips a step. You should actually design graphics that lend clarity, and that clarity can make a chart ‘simple’ to read. However, sometimes a dataset is complex, so the visualization must be complex. The visualization might still work if it provides useful insights that you wouldn’t get from a spreadsheet. […] Sometimes a table is better. Sometimes it’s better to show numbers instead of abstract them with shapes. Sometimes you have a lot of data, and it makes more sense to visualize a simple aggregate than it does to show every data point.” (Nathan Yau, “Data Points: Visualization That Means Something”, 2013)*

*“A boxplot is a dotplot enhanced with a schematic that provides information about the center and spread of the data, including the median, quartiles, and so on. This is a very useful way of summarizing a variable’s distribution. The dotplot can also be enhanced with a diamond-shaped schematic portraying the mean and standard deviation (or the standard error of the mean).” (Forrest W Young et al, “Visual Statistics: Seeing data with dynamic interactive graphics”, 2016)*

*“Just as with aggregated data, an average is a summary statistic that can tell you something about the data — but it is only one metric, and oftentimes a deceiving one at that. By taking all of the data and boiling it down to one value, an average (and other summary statistics) may imply that all of the underlying data is the same, even when it’s not.” (John H Johnson & Mike Gluck, “Everydata: The misinformation hidden in the little data you consume every day”, 2016)*

*“Dashboards are a type of multiform visualization used to summarize and monitor data. These are most useful when proxies have been well validated and the task is well understood. This design pattern brings a number of carefully selected attributes together for fast, and often continuous, monitoring — dashboards are often linked to updating data streams. While many allow interactivity for further investigation, they typically do not depend on it. Dashboards are often used for presenting and monitoring data and are typically designed for at-a-glance analysis rather than deep exploration and analysis.” (Danyel Fisher & Miriah Meyer, “Making Data Visual”, 2018)*

*“A data visualization, or dashboard, is great for summarizing or describing what has gone on in the past, but if people don’t know how to progress beyond looking just backwards on what has happened, then they cannot diagnose and find the ‘why’ behind it.” (Jordan Morrow, “Be Data Literate: The data literacy skills everyone needs to succeed”, 2021)*

*More quotes on “Aggregation” in Graphical Representation at **sql-troubles.blogspot.com**.*