In Excel: use the appropriate test function, such as =T.TEST(array1,array2,2,2) for a two-tailed two-sample t-test, and interpret the result against your chosen alpha level.
On this page7
Choose the statistical test before writing the formula.
Put each sample in a clean numeric range.
Use the matching Excel test function and tail/type arguments.
Report p-value with sample sizes and the test used.
Need it as an auditable file?
Ships inside the linked template — formula-driven, unlocked, audit-ready.
What this does
A p-value is not a generic spreadsheet statistic; it comes from a specific hypothesis test. Excel can calculate p-values with T.TEST, Z.TEST, CHISQ.TEST and Data Analysis ToolPak outputs. The right function depends on the design, distribution assumptions and whether the test is one-tailed or two-tailed. 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. The difference between a quick fix and a sheet you can trust is the extra minute you spend validating “p value from 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
Scores before training are in A2:A21 and after training in B2:B21 for separate groups. =T.TEST(A2:A21,B2:B21,2,2) returns the two-tailed p-value for a two-sample equal-variance t-test. If p is 0.03 and alpha is 0.05, the difference is statistically significant under that test. P-value content needs caution because users can easily produce a number that looks authoritative but comes from the wrong test. 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
If you are in Google Sheets rather than Excel, the good news is that the formula shown here is identical and the workflow barely changes — menus sit across the top instead of in a ribbon, and a few function names differ slightly, but anything you build here moves across with little or no rework. 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 and the detail above shows why it holds — so the next time a colleague asks, you can answer without reaching for search. The short version of “p value from excel”: the answer is at the top of this page, the tool proves it on your own numbers, and the sections above explain why it holds so the next variation does not stump you. Excel rewards people who reference cells instead of typing values and who keep inputs separate from formulas, because that is what makes a result you can audit months later. Build it once, deliberately, with the live tool as a check, and you convert a one-off lookup into a reusable skill — which is the whole point of learning the why and not just the what.
Common mistakes
- Using
T.TESTfor every p-value question. - Ignoring paired versus unpaired samples.
- Treating p-value as effect size.
- Changing one-tailed versus two-tailed after seeing results.
Frequently asked questions
Which Excel function gives a p-value?
It depends on the test: T.TEST, Z.TEST, CHISQ.TEST and ToolPak tests can return p-values.
Is p < 0.05 always significant?
Only relative to a prechosen alpha and suitable test assumptions.
Does p-value show importance?
No. It is evidence against a null hypothesis, not effect size.