It's what I was taught in statistics class at MIT. But you're right, I rarely see them, people just call bar charts "histograms". What it does is smooth out out

It's what I was taught in statistics class at MIT. But you're right, I rarely see them, people just call bar charts "histograms". What it does is smooth out outliers. There is always (i mean, within reason) a good sample at the mean, but as you go out from the mean you get more and more variance (it's sort of the definition of variance) A gaussian bell curve is a "population density function" and this definition of histogram reflects keeping the population density meaningful in terms of height. I will try to look for some. But the wikipedia article on histograms does reference them, that's what they are talking about wrt "varying width" bar charts. It's just that there is a rule to varying the width, "x times y is equal for all the rectangles" (oh yeah, you even get to "eyeball it" to make them. Unless you have a massive number of sample points, if you can choose a width parameter that makes your curve look normal, your curve probably is normal, or at least, you can't say that it's not, no matter what the bar chart looks like. to put it another way, if you follow this rule for a perfect normal distribution, you'll still get a plot of a perfect normal distribution. But if you have a sparse sample that's a jaggy bell curve, it will move the curve toward a smoother curve, accurate in the sense of "this curve is statistically equivalent to your data, so if it looks "normal", probably your data is normal, and you won't imagine a mode where there isn't one.