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Histogram
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==== Sturges's formula ==== [[Sturges's rule]]<ref name=sturges>{{cite journal |last=Sturges |first=H. A. |year=1926 |title=The choice of a class interval |journal=Journal of the American Statistical Association |volume=21 |issue=153 | pages=65–66 | jstor=2965501 | doi = 10.1080/01621459.1926.10502161 }}</ref> is derived from a [[binomial distribution]] and implicitly assumes an approximately normal distribution. :<math>k = \lceil \log_2 n \rceil+ 1 , \, </math> Sturges's formula implicitly bases bin sizes on the range of the data, and can perform poorly if {{math|''n'' < 30}}, because the number of bins will be small—less than seven—and unlikely to show trends in the data well. On the other extreme, Sturges's formula may overestimate bin width for very large datasets, resulting in oversmoothed histograms.<ref name="Scott2009WIRE">{{cite journal|last=Scott|first=David W.|title=Sturges' rule|journal = WIREs Computational Statistics|volume=1|issue=3|year=2009|pages=303–306 |doi=10.1002/wics.35|s2cid=197483064 }}</ref> It may also perform poorly if the data are not normally distributed. When compared to Scott's rule and the Terrell-Scott rule, two other widely accepted formulas for histogram bins, the output of Sturges's formula is closest when {{math|''n'' ≈ 100}}.<ref name="Scott2009WIRE"/>
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