Outlier Detector

100% client-side

Identify and isolate statistical anomalies in your experimental data or MD trajectories locally.

dataset.csv

0 Rows

Anomalies Detected 0

Target Variable

Algorithm

Threshold Multiplier

3.0

Flags values where |Z| > 3.0.

How this method decides

Data Array Preview

How-To Guide

How to detect outliers in your data

Upload a CSV, pick a column and a detection rule, and flag anomalous values, then export either the cleaned data or just the anomalies.

Load your data

Drag in a .csv/.tsv/.txt file (or click to browse). Or click Load example to try a sample with two planted outliers.

Pick a column & method

Choose the numeric column and one of Z-Score, IQR, or the robust Modified Z-Score.

Set the threshold

Adjust the multiplier; the hint explains what the current setting flags. Defaults follow standard practice.

Scan & export

Run the scan to highlight outliers in the table, then export the cleaned dataset or the flagged rows.

How it works

Three complementary rules

Z-Score assumes roughly normal data. IQR (Tukey) is non-parametric and good for skew. Modified Z-Score uses the median and MAD, so it is robust, a few extreme values don't hide the rest.

Why robustness matters

The mean and standard deviation are themselves distorted by outliers, so a single huge value can mask others. Median/MAD-based detection avoids this, which is why 3.5 on the modified Z-Score is a widely recommended default.

A quick illustration: in the built-in example the classic Z-Score (k=3) flags only the value 250, while the Modified Z-Score (k=3.5) also catches the low value 3, because the mean and SD were inflated by 250 itself. The preview highlights up to 200 rows; export always covers the full dataset.

Method & Equations

The three detection rules

Each method flags values in the selected column that lie unusually far from the centre; they differ in how “centre” and “spread” are estimated and how robust they are to the very outliers you are hunting.

Z-Score (parametric), uses the mean and sample standard deviation:

z_{i} = \frac{x_{i} - \bar{x}}{s}, flag if | z_{i} | > k

Tukey’s IQR fences (non-parametric), uses the quartiles:

flag if x_{i} < Q_{1} - k \cdot IQR or x_{i} > Q_{3} + k \cdot IQR, IQR = Q_{3} - Q_{1}

Modified Z-Score (robust), uses the median and the median absolute deviation:

M_{i} = \frac{0.6745 (x_{i} - \tilde{x})}{MAD}, MAD = \underset{}{median} | x_{i} - \tilde{x} |

where $\bar{x}$ is the mean, $s$ the sample standard deviation, $Q_{1}$ / $Q_{3}$ the first/third quartiles, $\tilde{x}$ the median, MAD the median absolute deviation, and $k$ the threshold you set. Typical defaults are $k = 3$ (Z-Score), $k = 1.5$ (IQR, Tukey) and $k = 3.5$ (modified Z-Score, Iglewicz–Hoaglin). The constant 0.6745 makes the MAD a consistent estimator of the standard deviation for normal data. When more than half the values are identical (MAD = 0) the tool falls back to the mean-absolute-deviation form.

References & documentation

Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley. (Origin of the IQR “fences”.)
Iglewicz, B., & Hoaglin, D. C. (1993). How to Detect and Handle Outliers. ASQC Quality Press. (Modified Z-score, threshold 3.5.)
Heckert, N. A., Filliben, J. J., Croarkin, C., et al. (2002). NIST/SEMATECH e-Handbook of Statistical Methods, §1.3.5 & §7.1.6. itl.nist.gov/div898/handbook.

Frequently asked questions

Is my data uploaded anywhere?

No. Parsing and detection run locally in your browser via PapaParse and jStat.

Which method should I use?

Z-Score for roughly normal data, IQR for skewed data, and the Modified Z-Score when you want robustness to the outliers themselves (a good general default).

What thresholds are typical?

k = 3 for Z-Score, k = 1.5 for Tukey's IQR fences, and k = 3.5 for the modified Z-Score (Iglewicz–Hoaglin).

How many values do I need?

At least four numeric values in the chosen column for a meaningful estimate of spread.

Does 'Export Cleaned' change my original file?

No. It downloads a new CSV with the flagged rows removed; your source file is untouched.

NIST e-Handbook Interquartile range Median absolute deviation

Detection runs entirely in your browser, no data leaves your device.