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<img src="/uploads/upload/image/5200/direct/1571100915243-1571100915243.png" alt="A theoretical heavy-tailed distribution curve showing a steep decline from left to right, with the highest concentration at the left (shown in bright green) gradually tapering off to a long tail extending far to the right. The curve demonstrates the characteristic shape of distributions where most values cluster at lower ranges while a small number of extreme values extend far into the upper range."> Protobi sets "Round by..." to **auto** which bins values into equal ranges. You can set "Round by..." to **log** for distributions with heavy tails. Learn more about the "Round by..." dialog in our ["Bin numeric values"](/edit-access/bin-numeric-values) tutorial. Protobi automatically bins numeric variables into ranges. Numeric variables come in a few varieties: - Constants (e.g. π = 3.141... ) - Light-tailed distributions - Heavy-tailed distributions Many variables we encounter in market research have light-tailed or even distributions, such as percentages, preference ratings, etc. Other variables such as number of patients, income, book sales, frequent flier miles, etc. have heavy-tail distributions. Benoit Mandelbrot coined the terms "mild" versus "wild" randomness to describe the difference. ## Example Below is an example where customers are asked for their purchase budget in dollars. This has a classic heavy-tail distribution with a small number of individuals with very large values. <img src="/uploads/upload/image/5200/direct/1571099607664-1571099607664.png" alt="A frequency table titled 'BUDGET_LINEAR' showing purchase budget data with Round By set to 'auto' (linear binning). The table displays value ranges from [NA] (20.1%) through bins like '1 to 10,000' (45.1%), '10,001 to 20,000' (19.9%), with decreasing frequencies as budget ranges increase. Most responses cluster in the lower ranges, with very small percentages in higher ranges like '60,001 to 70,000' (0.3%). The mean is 11,946." style="width: 480px;" class="fr-fic fr-dib"> By default, Protobi sets Round By = **auto**, which chooses linear bin sizes for numeric variables based on the standard deviation. We can see that many people have budgets of $1,000 to $5,000, and very few have budgets much over $30,000. The second version uses default binning with Round By set to **log** ,which chooses logarithmic bin sizes. In the graph below we can see that there are quite a number of customers willing to spend under $1,000, and also a substantial number that are willing to spend a lot more. <img src="/uploads/upload/image/5200/direct/1571099996430-1571099996430.png" alt="A frequency table titled 'BUDGET_LOG' showing the same purchase budget data with Round By set to 'log' (logarithmic binning). The table displays logarithmically-scaled value ranges from 0 (20.1%) through ranges like '100 to 249' (1.0%), '250 to 499' (1.7%), '2,500 to 4,999' (11.4%), '5,000 to 9,999' (18.3%), '10,000 to 24,999' (25.3%), with notable percentages distributed across all ranges. The mean is 9,546. This binning reveals a more nuanced distribution pattern than the linear version." style="width: 480px;" class="fr-fic fr-dib"> A product strategy might be radically different with this perspective, selling differently to customers with $250 versus $2,500 to spend, rather than lumping them all into an "Under $5,000" category.
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