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When deciding how to slice data in a histogram, you should consider the nature of your data and what you want to visualize. The size and number of bins can greatly affect the resulting histogram. Too many bins can over-complicate the data, while too few can oversimplify it. You should also consider the distribution of your data. If the data is not evenly distributed, you may need to adjust your bin sizes accordingly. Finally, consider the context and purpose of your analysis. Different scenarios may require different approaches to slicing data.
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Say you're a scientist, and you have a list of data that involves a specific gender and height. The first three histograms visualize the count of each height by gender, the distribution of the two genders, or the distribution of all the heights. But remember: these inputs can be customized to anything you want; say you run a warehouse, and you want to organize related parts by their respective sizes; delete the inputs in blue, and replace them with your specifications. Histograms work by separating data into groupings called bins. Here, we provide a simple filter to decide how to slice the data.
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