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Outlier Detection Calculator

Detect outliers using IQR bounds, z-score bounds, and modified z-score methods. Determine whether a data point is an outlier using configurable thresholds.

Inputs

Results

IQR Outlier?

No

Z-Score Outlier?

Yes

Modified Z Outlier?No
IQR Lower Bound-50
IQR Upper Bound150
Interquartile Range50
Z-Score2.5
Z Lower Bound20
Z Upper Bound80
Modified Z-Score0.91
How to Use This Calculator
  1. Enter the dataset values or summary statistics (mean and standard deviation).
  2. Set the Z-score threshold (typically 2.5-3.5) or IQR multiplier (typically 1.5).
  3. Review the number and percentage of detected outliers.
  4. Investigate outliers to determine if they are data errors or genuine extreme events.
  5. Decide whether to remove, cap, or retain outliers based on your analysis goals.

What each input means

Data Value
The data point you want to test for outlier status.
Q1 (25th Percentile)
The first quartile of your dataset — 25% of values fall below this.
Q3 (75th Percentile)
The third quartile of your dataset — 75% of values fall below this.
Mean
The arithmetic mean of your dataset for z-score calculations.
Standard Deviation
The standard deviation of your dataset.
Threshold Multiplier
Multiplier for IQR fences and z-score cutoff. Standard is 1.5 for IQR (mild outliers) or 3 for extreme outliers.

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    Data Value = 100, Q1 (25th Percentile) = 25, Q3 (75th Percentile) = 75, Mean = 50 = 6 input(s) provided
  2. Calculate IQR Outlier?
    No = No
  3. Calculate Z-Score Outlier?
    Yes = Yes
  4. Calculate Modified Z Outlier?
    No = No
  5. Calculate IQR Lower Bound
    IQR Lower Bound
    -50 = -50

Engine last updated . Checked against 1 independently-derived test — how we verify calculators.

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