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Median, Quartiles & IQR Calculator

Paste a dataset and the tool sorts it, then reports the median (Q2), the first and third quartiles Q1 and Q3, the inter-quartile range IQR, the minimum and maximum, and the Tukey 1.5×IQR outlier fences. The five-number summary it prints is exactly the five lines of a box plot, and every value outside the fences is listed so you can spot-clean the data during exploratory analysis.

Median (Q2)

40.5

Q1 (first quartile)

36

Q3 (third quartile)

43

Five-number summary + Tukey fences

Minimum
Inter-quartile range IQR
Maximum
Lower fence Q1 − 1.5·IQR
Upper fence Q3 + 1.5·IQR

Tukey 1.5×IQR outliers

Formula

Median Q2 = middle value of sorted sample (mean of the middle two when n is even) Q1 = median of lower half Q3 = median of upper half IQR = Q3 − Q1 Lower fence = Q1 − 1.5·IQR Upper fence = Q3 + 1.5·IQR

Frequently asked

Why do different tools give different Q1 / Q3 values?

Because "quartile" has several mathematically valid definitions. Hyndman & Fan (1996) catalogue nine formulas; the three most common are: (1) Tukey hinges — used by R's fivenum() and by this tool — which splits the sample in two and takes the median of each half; (2) Type 7 (NumPy default, Excel QUARTILE.INC) — linear interpolation at position (n−1)·p; (3) Type 6 (Minitab, SPSS) — interpolation at position (n+1)·p. On large continuous samples the differences are tiny; on small samples (n < 20) the methods can disagree by several percent. The Tukey method has the virtue of being conceptually simple ("the median of each half") and matching the original definition used for box plots.

Should I use the IQR or the standard deviation to summarise spread?

Both have their place. The standard deviation σ pairs with the mean and is most useful when the data is roughly normal — together with the 68-95-99.7 rule it gives an intuitive feel for probabilities. The IQR pairs with the median and is robust to extreme values and to skew, which is why box plots use the IQR rather than σ. Rule of thumb: inspect a histogram or QQ-plot first — if the data is symmetric and not heavy-tailed either summary works; for clearly skewed data or data with outliers (incomes, dwell times, earthquake magnitudes) prefer median + IQR. Reporting both is also fine: σ ≈ 1.349 · IQR for a normal distribution, a handy sanity check.

Should I always remove the values the tool flags as outliers?

No. The Tukey 1.5×IQR rule is an exploratory flag, not a verdict: it says "this point sits apart from the rest" — not "this point is wrong". Standard practice: (1) check provenance — measurement unit, transcription error, sensor glitch? Fix or drop if so; (2) bring in domain knowledge — financial black swans or biological hyper-responders are often the most informative observations; (3) report both versions — analyses with and without the flagged points let the reader judge. If the goal is a standard "normal-theory" test (t-test, ANOVA), a robust alternative (Wilcoxon, trimmed mean) is usually a better answer than ad-hoc deletion.

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