I don't get how you can be so confident about a concept you only vaguely remember from a class you took long ago, and that even after reading the relevant article on wikipedia you conclude that therefore wikipedia must be wrong rather than thinking "hmm, wonder whether I'm misremembering things here..."
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.
> The bins (intervals) must be adjacent and are often (but not required to be) of equal size.
And this:
> A histogram is used for continuous data, where the bins represent ranges of data, while a bar chart is a plot of categorical variables.
To be fair, there probably are variations in nomenclature across fields. But saying that a histogram with bins of equal width is a bar chart is wrong, at least in the general sense.