Drag, add, or remove data points and watch the mean, median, and trend lines update when you drop them. Toggle between Z-Score and IQR methods to see which points get flagged, and why the answers can differ.
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15 points · 0 flagged
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Z-Score Method
Measures how many standard deviations each coordinate is from that axis mean, applied to X and Y separately. Assumes a roughly normal distribution. A key limitation: extreme outliers inflate the standard deviation, which can actually mask themselves.
Best for
Normally distributed data
Formula
z = (x − μ) / σ
Limitation
Outliers inflate σ, masking themselves
Used in
Quality control, anomaly detection
Detection Method
Mean (X / Y)
55.0 / 45.3
Std Dev (X / Y)
17.2 / 16.8
X fences
3.5 to 106.5
Y fences
-5.2 to 95.9
Flags points more than 3.0σ from the mean on either axis. Assumes a roughly normal distribution per axis.
Flagged Outliers
0 / 15No outliers detected with current settings
Live Statistics (X-axis)
Mean X
55.0
sensitive to outliers
Median X
50.0
robust to outliers
Std Dev X
17.2
spread of values
N points
15
0 flagged
No outliers flagged: mean and median are both reliable.
When to Use Which