Calculator
Example data table
| Dataset | Trim setting | Expected trimmed mean |
|---|---|---|
| 12, 14, 15, 16, 18, 19, 21, 22, 100 | Count trim = 1 per tail | Trim 12 and 100; mean of remaining is 17.8571 |
| 2, 2, 3, 3, 3, 4, 50 | Percent trim = 20% | Trims one value each tail; trimmed mean is 3.0 |
Formula used
Sort the data in ascending order. Remove k smallest and k largest values. Compute the mean of the remaining values.
If you trim by percent, k = ⌊(p/100/2)·n⌋ where p is total trim percent.
How to use this calculator
- Paste numbers into the data box using commas or new lines.
- Select percent trim or count trim, then set the value.
- Choose options like rounding and tail details if needed.
- Press Submit to view results above the form.
- Use Download CSV or PDF to share outputs.
Interpretation notes
- Trimmed mean is useful when a few points are extreme outliers.
- Too much trimming can hide real signal and reduce sample size.
- Compare mean, median, and winsor mean to spot skew.
- The confidence interval shown uses a normal approximation.
Why trimmed means improve robust summaries
Real-world datasets often contain recording errors, rare events, or sudden spikes. A trimmed mean reduces the influence of those extremes by discarding equal numbers of the smallest and largest observations. This creates a central average that is typically closer to the typical case than the simple mean, especially in skewed distributions.
Selecting percent versus count trimming
Percent trimming is convenient when you want a consistent rule across datasets of varying size, such as trimming 10% overall. Count trimming is better for small samples where you know exactly how many observations should be excluded from each tail. The calculator shows the effective trim percentage so you can compare settings transparently.
Interpreting results alongside median and winsor mean
The median is maximally resistant to outliers but can ignore meaningful variation. The trimmed mean balances resistance and efficiency by using most values while removing extremes. The winsor mean replaces extremes with boundary values, keeping sample size unchanged. Comparing these three helps diagnose whether extremes dominate the average or whether the center is stable.
Quality checks using sorted and trimmed lists
Auditability matters in analytics workflows. The sorted list reveals where extremes sit relative to the bulk of the data. The trimmed list shows exactly which values contribute to the reported trimmed mean. When the “show removed tails” option is enabled, you can document excluded values and justify the trimming rule in a reproducible manner.
Practical guidance for reporting and decision making
Use trimmed means for metrics like response times, transaction values, or sensor readings when a small fraction of observations are unusually large or small. Avoid excessive trimming when extremes reflect real risk or opportunity. Pair the trimmed mean with spread measures such as standard deviation and interquartile range to communicate uncertainty and variability to stakeholders. When datasets are updated frequently, keep the trim rule fixed so trends remain comparable. For benchmarking, report both the untrimmed mean and the trimmed mean; the gap quantifies outlier pressure. In experiments, prefer the same trimming across groups over time.
FAQs
1) What is a trimmed mean used for?
It summarizes central tendency while reducing the impact of extreme values. It is helpful for skewed data, noisy measurements, and operational metrics where rare spikes would distort the simple mean.
2) How much trimming should I apply?
Start with 5% to 20% total trimming and compare results with the mean and median. Use smaller trims for small samples, and avoid heavy trimming when extremes represent meaningful outcomes.
3) What is the difference between trimming and winsorizing?
Trimming removes observations from both tails before averaging. Winsorizing keeps all observations but replaces tail values with boundary values, so the sample size stays unchanged.
4) Why does the calculator show an effective trim percentage?
Percent and count settings can map to different tail removals depending on sample size. The effective percentage makes the rule transparent and supports consistent reporting across datasets.
5) Can I use this on non-normal data?
Yes. Trimmed means are often more stable than means when distributions are skewed or heavy tailed. They still assume the middle portion represents the process you want to summarize.
6) How should I report trimmed mean results?
State the trimming rule, the sample size, and whether trimming was percent or count based. Provide the untrimmed mean and a spread measure such as IQR or standard deviation for context.