"Adjusting scale is an important practice in data visualization. While the log transform is versatile, it doesn’t handle all situations where skew or curvature occurs. For example, at times the values are all roughly the same order of magnitude and the log transformation has little impact. Another transformation to consider is the square root transformation, which is often useful for count data." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"As data scientists, we create data visualizations in order to understand our data and explain our analyses to other people. A plot should have a message, and it’s our job to communicate this message as clearly as possible." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Box plots (also known as box-and-whisker plots) give a visual summary of a few important statistics of a distribution. The box denotes the 25th percentile, median, and 75th percentile, the whiskers show the tails, and unusually large or small values are also plotted. Box plots cannot reveal as much shape as a histogram or density curve. They primarily show symmetry and skew, long/short tails, and unusually large/small values (also known as outliers)." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Ignoring sampling weights can give a misleading presentation of a distribution. Whether for a histogram, bar plot, box plot, two-dimensional contour, or smooth curve, we need to use the weights to get a representative plot." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"It’s important to choose a perceptually uniform color palette. By this we mean that when a data value is doubled, the color in the visualization looks twice as colorful to the human eye. We also want to avoid colors that create an afterimage when we look from one part of the graph to another, colors of different intensities that make one attribute appear more important than another, and colors that colorblind people have trouble distinguishing between. We strongly recommend using a palette or a palette generator made specifically for data visualizations." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Many people mistakenly think that the defining property of a simple random sample is that every unit has an equal chance of being in the sample. However, this is not the case. A simple random sample of n units from a population of N means that every possible col‐lection of n of the N units has the same chance of being selected. A slight variant of this is the simple random sample with replacement, where the units/marbles are returned to the urn after each draw. This method also has the property that every sample of n units from a population of N is equally likely to be selected. The difference, though, is that there are more possible sets of n units because the same marble can appear more than once in the sample." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Researchers have studied how accurately people can read information displayed in different types of plots. They have found the following ordering, from most to leasta ccurately judged (•) Positions along a common scale, like in a rug plot, strip plot, or dot plot (•) Positions on identical, nonaligned scales, like in a bar plot (•) Length, like in a stacked bar plot (•) Angle and slope, like in a pie chart (•) Area, like in a stacked line plot or bubble chart (•) Volume and density, like in a three-dimensional bar plot (•) Color saturation and hue, like when overplotting with semitransparent points." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Several key assumptions enter into this urn model, such as the assumption that the vaccine is ineffective. It’s important to keep track of the reliance on these assumptions because our simulation study gives us an approximation of the rarity of an outcome like the one observed only under these key assumptions." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Shape matters because models and statistics based on symmetric distributions tend to have more robust and stable properties than highly skewed distributions" (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Side-by-side box plots offer a similar comparison of distributions across groups. The box plot offers a simpler approach that can give a crude understanding of a distribution. Likewise, violin plots sketch density curves along an axis for each group. The curve is flipped to create a symmetric 'violin' shape. The violin plot aims to bridge the gap between the density curve and box plot." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Smoothing and aggregating can help us see important features and relationships, but when we have only a handful of observations, smoothing techniques can be misleading. With just a few observations, we prefer rug plots over histograms, box plots, and density curves, and we use scatterplots rather than smooth curves and density contours. This may seem obvious, but when we have a large amount of data, the amount of data in a subgroup can quickly dwindle. This phenomenon is an example of the curse of dimensionality." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"Stacked line plots are even more difficult to read because we have to judge the gap between curves as they jiggle up and down." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"We divide accuracy into two basic parts: bias and precision (also known as variation). Our goal is for the darts to hit the bullseye on the dart‐ board and for the bullseye to line up with the unseen target. The spray of the darts on the board represents the precision in our measurements, and the gap from the bulls‐eye to the unknown value that we are targeting represents the bias." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"When interpreting a histogram or density curve, we examine the symmetry and skewness of the distribution; the number, location, and size of high-frequency regions (modes); the length of tails (often in comparison to a bell-shaped curve); gaps where no values are observed; and unusually large or anomalous values." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"When we examine relationships between qualitative features, we examine proportions of one feature within subgroups defined by another. In the previous section, the three line plots in one figure and the side-by-side bar plots both display such comparisons. With three (or more) qualitative features, we can continue to subdivide the data according to the combinations of levels of the features and compare these proportions using line plots, dot plots, side-by-side bar charts, and so forth. But these plots tend to get increasingly difficult to understand with further subdivisions." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
"With qualitative data, the bar plot serves a similar role to the histogram. The bar plot gives a visual presentation of the 'popularity' or frequency of different groups. However, we cannot interpret the shape of the bar plot in the same way as a histogram. Tails and symmetry do not make sense in this setting. Also, the frequency of a category is represented by the height of the bar, and the width carries no information. The two bar charts that follow display identical information about the number of breeds in a category; the only difference is in the width of the bars. In the extreme, the rightmost plot eliminates the bars entirely and represents each count by a single dot." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)
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