What a histogram shows that a list of numbers does not
A histogram turns a column of numbers into a picture of their shape. It shows where the values cluster, how far they spread, and whether the distribution leans to one side. You can also see if there are two humps or a single value far from the rest. A list of numbers hides all of that.
This tool is for one column of numbers, one row per observation. It counts how many values fall in each range. You do not need to sort or count anything yourself. Paste the column and the chart appears.
The tool reads pasted text from Excel, Google Sheets, Numbers, or a plain text file. It finds the delimiter on its own. A single column of numbers needs no delimiter at all. If a first row is text and the rows below are numbers, it is treated as headings. Empty cells and spreadsheet errors like #DIV/0! are counted as empty, not as zero.
How bins work and why they change the picture
Bins are the ranges that divide your data. Each bar shows how many values fall in one range. The width of the bins changes how the histogram looks. Wide bins can hide a second hump. Narrow bins can make a smooth distribution look jagged.
The default bin width comes from the Freedman-Diaconis rule: two times the interquartile range divided by the cube root of the number of observations [Freedman & Diaconis 1981]. The interquartile range is the spread of the middle half of your data. It ignores extreme values, so one outlier does not flatten every bin. Sturges' older rule uses the full range and is less resistant to outliers [Sturges 1926].
When the interquartile range is zero, because most values are identical, the tool falls back to Sturges' rule. The bin width is then snapped to 1, 2, 2.5, or 5 times a power of ten. The bins start at a round number, so edges read "0 to 5, 5 to 10" instead of "0 to 3.333". The number of bins you ask for is a target. The actual number used is reported.
Bins are right-open. A value on a boundary falls in the upper bin, so no value is counted twice. The top bin closes at its upper edge, so the largest value is included.
Bin width changes the apparent shape of a distribution. Try two or three bin counts before you trust a shape. A pattern that only appears at one bin width may not be real.
Worked example
Suppose you paste these 10 numbers: 2, 3, 3, 4, 5, 5, 6, 7, 8, 20. The interquartile range is the spread of the middle half. The first quartile is 3, the third quartile is 7, so the interquartile range is 4. The cube root of 10 is about 2.15. Two times 4 divided by 2.15 gives a bin width of about 3.7. The tool snaps that to 5. Bins might run 0 to 5, 5 to 10, 10 to 15, 15 to 20. The value 20 sits in the top bin. The histogram shows most values between 0 and 10, with one value far to the right.
Summary statistics and the standard deviation
The tool shows the mean, median, smallest, and largest values. The standard deviation is the sample one, dividing by n minus 1, because your pasted column is a sample. Quartiles use linear interpolation between order statistics, which is R's default type 7 and the definition Excel's PERCENTILE function uses [Hyndman & Fan 1996]. If you check the numbers in your spreadsheet, you should get the same answers.
Histogram or bar chart?
A histogram is not a bar chart. A bar chart compares categories you supply, like sales by region. A histogram counts how many of your values fall in each range. The bars touch because the ranges are continuous. If you have categories and amounts, use a bar chart instead. If you have one column of numbers and want to see their shape, a histogram is the right choice.
For more on how the tool reads numbers, dates, and delimiters, see how it works.