In Excel: compute the mean and standard deviation, build a column of x values across the range, get the height of the curve with =NORM.DIST(x, mean, sd, FALSE), then plot x against that column as a Scatter with Smooth Lines.
On this page7
Mean = 70.714 · Std deviation = 5.992
What this does
A bell curve is the plot of the normal distribution defined by your data's mean and standard deviation. Excel has no "insert bell curve" button, so you build it: AVERAGE and STDEV.S give the two parameters, a column of evenly spaced x values spans roughly mean ± 3 standard deviations, and NORM.DIST with its last argument set to FALSE returns the probability density — the curve's height — at each x. Setting that argument to TRUE instead gives the cumulative S-curve. Plotting the result needs Scatter with Smooth Lines; a line chart treats the x values as categories and distorts the shape. Keep the inputs visible and clearly labelled and the whole thing stays auditable — anyone who opens the file later, including you, can see at a glance exactly what feeds the result and change one assumption without hunting through the formula. For “make gaussian curve in excel”, the reliable version is a short checking loop, not just the first command that appears to work. Run it on a deliberately small range first, watch how the affected cells change, and only then apply the same setup to the full sheet. When a formula is involved, keep the inputs labelled beside it, reference cells instead of typing values, and apply number formatting only after the result checks out. That is what makes a layout choice that keeps the sheet readable and sortable useful in real work: repeatable, auditable, and not dependent on memory or luck.
A worked example
200 test scores in A2:A201. D1 = =AVERAGE(A2:A201) → 72.4, D2 = =STDEV.S(A2:A201) → 11.8. In F2 put =$D$1-3*$D$2 and in F3 =F2+($D$2*6/60), filled down to F62 — 61 points spanning ±3σ. In G2: =NORM.DIST(F2, $D$1, $D$2, FALSE), filled down. Select F2:G62 and Insert ▸ Scatter ▸ Scatter with Smooth Lines. The peak sits at 72.4 and the curve is near zero by 37 and 108. A bell curve is how a set of measurements gets turned into a statement about how likely a value is — grading, quality limits and process control all start with the mean, the standard deviation and this shape. A practical tip before you scale it up: build it once on a small block of test data, confirm the number against the tool on this page, and only then point it at your real sheet. That one habit catches almost every mistake while it is still cheap to fix, long before a wrong figure reaches a report or a colleague.
In Google Sheets
Everything above works in Google Sheets too. Excel and Sheets share the formula syntax used here; only the surrounding menus are arranged differently. That portability is deliberate — learn it once and it follows you between the two tools and across Windows and Mac. Keep this page bookmarked for the next time the same question comes up. Better still, rebuild the example once in your own sheet — doing it yourself, with the tool above to check against, is what turns a copied formula into a technique you own. If you take one thing from this page on “make gaussian curve in excel”, make it the habit rather than the keystrokes: set the problem up with labelled inputs, reference those cells, and let Excel do the recomputing. Bookmark the page for the syntax, but do the example once in a blank sheet and check it against the tool above — five minutes of hands-on practice fixes the method in memory far better than re-reading, and it surfaces the small snags while they are still harmless. After that the technique is genuinely yours: faster than searching for it again, and reliable enough to drop into work that other people depend on.
Common mistakes
- Using a Line chart instead of a Scatter chart, which spaces the x values evenly by position and misshapes the curve.
- Passing TRUE as the fourth
NORM.DISTargument, which draws the cumulative distribution rather than the bell. - Using STDEV.P on a sample; STDEV.S is the right choice unless the data really is the whole population.
- Plotting a bell curve over data that is visibly skewed or bimodal, which asserts a normality the data does not have.
- Leaving the x values unsorted, which makes the smooth line double back on itself.
Frequently asked questions
How do I make a bell curve in Excel?
Compute the mean and standard deviation, build a column of x values across mean ± 3 standard deviations, calculate =NORM.DIST(x, mean, sd, FALSE) beside it, and plot the two columns as a Scatter with Smooth Lines.
What does the FALSE argument do in NORM.DIST?
It asks for the probability density — the height of the bell at that x. TRUE returns the cumulative probability instead, which draws an S-shaped curve.
How do I overlay my actual data?
Add a histogram of the raw values as a second series and put it on a secondary axis. Counts and probability densities are not on the same scale, so one axis would flatten the curve.
How wide should the x range be?
Mean ± 3 standard deviations covers about 99.7% of a normal distribution, which is enough for the curve to reach the baseline at both ends.