How to Calculate Kurtosis in Excel

What this does

SKEW measures how lopsided a distribution is. Zero means symmetric, positive means a long right tail (a few very large values, typical of income and response times), negative means a long left tail. KURT measures tail weight relative to a normal distribution, where positive means more extreme outliers than normal. Their practical value is as a check before applying any method that assumes normality: a skew beyond roughly ±1 says the mean is a poor summary and the median should lead instead. SKEW needs at least three values, KURT at least four. The same idea underpins a lot of everyday Excel work, so the few minutes spent getting it right here pay back across every sheet you build afterwards. Treat it as a pattern, not a one-off, and it stops being something you look up and starts being something you reach for. The difference between a quick fix and a sheet you can trust is the extra minute you spend validating “calculate kurtosis in excel”. Start on a copy or a tiny sample, keep the affected cells visible, and compare the result with the tool above before you touch the real workbook. 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. The point is a calculation you can defend to a CFO or an auditor, but the practical win is that someone else can open the file and understand what happened without asking you.

A worked example

Testing whether a mean is representative: =SKEW(B2:B500) returning 1.8 says the data has a long right tail and the median is the honest headline. Confirming with the two summaries: a large gap between =AVERAGE(B2:B500) and =MEDIAN(B2:B500) tells the same story. =KURT(B2:B500) above 3 warns of more extreme outliers than a normal distribution would produce. SKEW answers "is the mean lying to me", which is the question that should precede every reported average. One habit worth forming early: name the cells that hold your inputs, so the formula reads in plain language instead of a string of cell addresses. A reviewer — or you in three months — can then follow the logic without decoding what B7 and D2 were supposed to mean, which is most of what makes a sheet maintainable.

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. The aim was to get you unstuck fast and leave you a little more capable than a copy-paste would. The answer is at the top, the tool proves it, and the detail above shows why it holds — so the next time a colleague asks, you can answer without reaching for search. If you take one thing from this page on “calculate kurtosis 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

• Reporting a mean on data with a skew beyond ±1, which describes almost nobody in the dataset.
• Running them on fewer than three or four values, which returns #DIV/0!.
• Treating a skew near zero as proof of normality; it rules out one departure, not all of them.

Frequently asked questions