A data scientist is performing exploratory data analysis on a dataset of e-commerce transaction amounts. They generate a histogram to understand the distribution of the transaction values, which are continuous and highly right-skewed. The initial plot, created using the default settings of a popular data visualization library, shows nearly all the data points clustered into a single bar on the far left, with a few other bars sparsely populated to the right. Which of the following is the most effective next step to improve the visualization and gain a clearer understanding of the data's distribution?
Adjust the binning strategy by experimenting with different bin widths or applying a rule like the Freedman-Diaconis rule.
Increase the number of bins to the maximum allowable value to ensure maximum granularity.
Replace the histogram with a box and whisker plot to better visualize the median and interquartile range.
Switch to a density plot, as histograms are not suitable for visualizing skewed continuous data.
The correct answer is to experiment with different bin widths or use a binning rule specifically designed for skewed data. In a histogram, the way data is grouped into bins is critical for its interpretation. With highly skewed data, default binning algorithms (which often assume a somewhat normal distribution) can create misleading visualizations. A very large bin width might group all the smaller, more frequent values into one bar, while the long tail of larger, infrequent values is spread thinly across the remaining bins, obscuring the details of the distribution. Adjusting the number of bins, or the width of each bin, allows for a more granular view. For right-skewed data, using more bins or applying a transformation (like a logarithmic scale on the x-axis, which is conceptually similar to changing bin widths on a log scale) can help to spread out the clustered data and make the distribution's shape more apparent.
Using a box plot is a plausible option for skewed data but it summarizes the distribution into quartiles and may hide features like bimodality, which a well-constructed histogram could reveal. Simply increasing the number of bins without considering the data's skewness might lead to a noisy, difficult-to-interpret plot. A density plot is a good alternative, but adjusting the histogram's parameters is the most direct and fundamental step to address the described problem with the initial histogram itself.
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