Outlier Detection Template
Flag values that sit well outside the normal range automatically — find the errors and surprises hiding in your data.
Prefer the spreadsheet? Anomaly Detection Template · Descriptive Statistics Template · Rolling Z-Score & MAD Outlier Detection · SPC Control Chart (X-bar & R) · Attendance & Persistent Absence Template · Absence & Bradford Factor · Salary Benchmarking & Pay Gap Analysis — free with an account, 3 free downloads.
See inside before you download: Anomaly Detection Template · Descriptive Statistics Template · Rolling Z-Score & MAD Outlier Detection · SPC Control Chart (X-bar & R) · Attendance & Persistent Absence Template · Absence & Bradford Factor · Salary Benchmarking & Pay Gap Analysis
What's inside this template
How it works
It loads with realistic sample data so you see a finished result immediately — nothing to set up.
Type over the sample values, or upload your own file and map the columns in a couple of clicks.
The Anomaly Detection tool recalculates instantly. Export to Excel or send a live link.
Related templates
Common questions
How are outliers detected?
By how far a value sits from the centre of its own column, using the interquartile range or standard deviations. Both are fixed rules rather than judgement, so the same data always flags the same rows.
Should I delete outliers?
Investigate first. A genuine data-entry error should go; a real extreme value is often the most informative row in the file. Deleting outliers by default is how you lose your biggest customer from the analysis.
What's the difference between an outlier and an anomaly?
An outlier is unusual compared to the rest of the column. An anomaly is unusual compared to its own expected pattern — a normal-looking sales figure in a month that should have been a peak. This template finds the first.
Start from the Anomaly / outlier scan template
Open it with sample data, swap in your own, and export in seconds — no formulas, no blank page.
Use this template free →