A histogram is a visual representation of the distribution of numeric data. The term was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to "bin" (or "bucket") the range of values— divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent and are often (but not required to be) of equal size.[2]

Bins are typically of equal width, but unequal bin sizes are sometimes used.

Histograms give a rough sense of the density of the underlying distribution of the data, and often for density estimation: estimating the probability density function of the underlying variable. The total area of a histogram used for probability density is always normalized to 1. If the length of the intervals on the x-axis are all 1, then a histogram is identical to a relative frequency plot.

Histograms are sometimes confused with bar charts. A histogram is used for quantitative data, where the bins represent ranges of values, while a bar chart is a plot of categorical variables. Some authors recommend that bar charts have gaps between the rectangles to clarify the distinction.[3][4]

A bar graph and a histogram are two common types of graphical representations of data. While they may look similar, there are some key differences between the two that are important to understand.

A bar graph is a chart that uses bars to represent the frequency or quantity of different categories of data. The bars can be either vertical or horizontal, and they are typically arranged either horizontally or vertically to make it easy to compare the different categories. Bar graphs are useful for displaying data that can be divided into discrete categories, such as the number of students in different grade levels at a school.

A histogram, on the other hand, is a graph that shows the distribution of numerical data. It is a type of bar chart that shows the frequency or number of observations within different numerical ranges, called bins. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The histogram provides a visual representation of the distribution of the data, showing the number of observations that fall within each bin. This can be useful for identifying patterns and trends in the data, and for making comparisons between different datasets.[5]


This is the data for the histogram to the right, using 500 items:

Bin/Interval Count/Frequency
−3.5 to −2.51 9
−2.5 to −1.51 32
−1.5 to −0.51 109
−0.5 to 0.49 180
0.5 to 1.49 132
1.5 to 2.49 34
2.5 to 3.49 4

The words used to describe the patterns in a histogram are: "symmetric", "skewed left" or "right", "unimodal", "bimodal" or "multimodal".

It is a good idea to plot the data using several different bin widths to learn more about it. Here is an example on tips given in a restaurant.

The U.S. Census Bureau found that there were 124 million people who work outside of their homes.[6] Using their data on the time occupied by travel to work, the table below shows the absolute number of people who responded with travel times "at least 30 but less than 35 minutes" is higher than the numbers for the categories above and below it. This is likely due to people rounding their reported journey time.[citation needed] The problem of reporting values as somewhat arbitrarily rounded numbers is a common phenomenon when collecting data from people.[citation needed]

Histogram of travel time (to work), US 2000 census. Area under the curve equals the total number of cases. This diagram uses Q/width from the table.
Data by absolute numbers
Interval Width Quantity Quantity/width
0 5 4180 836
5 5 13687 2737
10 5 18618 3723
15 5 19634 3926
20 5 17981 3596
25 5 7190 1438
30 5 16369 3273
35 5 3212 642
40 5 4122 824
45 15 9200 613
60 30 6461 215
90 60 3435 57

This histogram shows the number of cases per unit interval as the height of each block, so that the area of each block is equal to the number of people in the survey who fall into its category. The area under the curve represents the total number of cases (124 million). This type of histogram shows absolute numbers, with Q in thousands.

Histogram of travel time (to work), US 2000 census. Area under the curve equals 1. This diagram uses Q/total/width (crowding) from the table. The height of a block represents crowding which is defined as - percentage per horizontal unit.
Data by proportion
Interval Width Quantity (Q) Q/total/width
0 5 4180 0.0067
5 5 13687 0.0221
10 5 18618 0.0300
15 5 19634 0.0316
20 5 17981 0.0290
25 5 7190 0.0116
30 5 16369 0.0264
35 5 3212 0.0052
40 5 4122 0.0066
45 15 9200 0.0049
60 30 6461 0.0017
90 60 3435 0.0005

This histogram differs from the first only in the vertical scale. The area of each block is the fraction of the total that each category represents, and the total area of all the bars is equal to 1 (the fraction meaning "all"). The curve displayed is a simple density estimate. This version shows proportions, and is also known as a unit area histogram.

In other words, a histogram represents a frequency distribution by means of rectangles whose widths represent class intervals and whose areas are proportional to the corresponding frequencies: the height of each is the average frequency density for the interval. The intervals are placed together in order to show that the data represented by the histogram, while exclusive, is also contiguous. (E.g., in a histogram it is possible to have two connecting intervals of 10.5–20.5 and 20.5–33.5, but not two connecting intervals of 10.5–20.5 and 22.5–32.5. Empty intervals are represented as empty and not skipped.)[7]

Mathematical definitions

An ordinary and a cumulative histogram of the same data. The data shown is a random sample of 10,000 points from a normal distribution with a mean of 0 and a standard deviation of 1.

The data used to construct a histogram are generated via a function mi that counts the number of observations that fall into each of the disjoint categories (known as bins). Thus, if we let n be the total number of observations and k be the total number of bins, the histogram data mi meet the following conditions:

A histogram can be thought of as a simplistic kernel density estimation, which uses a kernel to smooth frequencies over the bins. This yields a smoother probability density function, which will in general more accurately reflect distribution of the underlying variable. The density estimate could be plotted as an alternative to the histogram, and is usually drawn as a curve rather than a set of boxes. Histograms are nevertheless preferred in applications, when their statistical properties need to be modeled. The correlated variation of a kernel density estimate is very difficult to describe mathematically, while it is simple for a histogram where each bin varies independently.

An alternative to kernel density estimation is the average shifted histogram,[8] which is fast to compute and gives a smooth curve estimate of the density without using kernels.

Cumulative histogram

A cumulative histogram is a mapping that counts the cumulative number of observations in all of the bins up to the specified bin. That is, the cumulative histogram Mi of a histogram mj is defined as:

Number of bins and width

There is no "best" number of bins, and different bin sizes can reveal different features of the data. Grouping data is at least as old as Graunt's work in the 17th century, but no systematic guidelines were given[9] until Sturges' work in 1926.[10]

Using wider bins where the density of the underlying data points is low reduces noise due to sampling randomness; using narrower bins where the density is high (so the signal drowns the noise) gives greater precision to the density estimation. Thus varying the bin-width within a histogram can be beneficial. Nonetheless, equal-width bins are widely used.

Some theoreticians have attempted to determine an optimal number of bins, but these methods generally make strong assumptions about the shape of the distribution. Depending on the actual data distribution and the goals of the analysis, different bin widths may be appropriate, so experimentation is usually needed to determine an appropriate width. There are, however, various useful guidelines and rules of thumb.[11]

The number of bins k can be assigned directly or can be calculated from a suggested bin width h as:

Histogram data represented with different bin widths

The braces indicate the ceiling function.

Square-root choice

which takes the square root of the number of data points in the sample (used by Excel's Analysis Toolpak histograms and many other) and rounds to the next integer.[12]

Sturges' formula

Sturges' formula[10] is derived from a binomial distribution and implicitly assumes an approximately normal distribution.

Sturges' formula implicitly bases bin sizes on the range of the data, and can perform poorly if 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' formula may overestimate bin width for very large datasets, resulting in oversmoothed histograms.[13] 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' formula is closest when n ≈ 100.[13]

Rice rule

The Rice Rule [14] is presented as a simple alternative to Sturges' rule.

Doane's formula

Doane's formula[15] is a modification of Sturges' formula which attempts to improve its performance with non-normal data.

where is the estimated 3rd-moment-skewness of the distribution and

Scott's normal reference rule

Bin width is given by

where is the sample standard deviation. Scott's normal reference rule[16] is optimal for random samples of normally distributed data, in the sense that it minimizes the integrated mean squared error of the density estimate.[9]

Freedman–Diaconis' choice

The Freedman–Diaconis rule gives bin width as:[17][9]

which is based on the interquartile range, denoted by IQR. It replaces 3.5σ of Scott's rule with 2 IQR, which is less sensitive than the standard deviation to outliers in data.

Minimizing cross-validation estimated squared error

This approach of minimizing integrated mean squared error from Scott's rule can be generalized beyond normal distributions, by using leave-one out cross validation:[18][19]

Here, is the number of datapoints in the kth bin, and choosing the value of h that minimizes J will minimize integrated mean squared error.

Shimazaki and Shinomoto's choice

The choice is based on minimization of an estimated L2 risk function[20]

where and are mean and biased variance of a histogram with bin-width , and .

Variable bin widths

Rather than choosing evenly spaced bins, for some applications it is preferable to vary the bin width. This avoids bins with low counts. A common case is to choose equiprobable bins, where the number of samples in each bin is expected to be approximately equal. The bins may be chosen according to some known distribution or may be chosen based on the data so that each bin has samples. When plotting the histogram, the frequency density is used for the dependent axis. While all bins have approximately equal area, the heights of the histogram approximate the density distribution.

For equiprobable bins, the following rule for the number of bins is suggested:[21]

This choice of bins is motivated by maximizing the power of a Pearson chi-squared test testing whether the bins do contain equal numbers of samples. More specifically, for a given confidence interval it is recommended to choose between 1/2 and 1 times the following equation:[22]

Where is the probit function. Following this rule for would give between and ; the coefficient of 2 is chosen as an easy-to-remember value from this broad optimum.


A good reason why the number of bins should be proportional to is the following: suppose that the data are obtained as independent realizations of a bounded probability distribution with smooth density. Then the histogram remains equally "rugged" as tends to infinity. If is the "width" of the distribution (e. g., the standard deviation or the inter-quartile range), then the number of units in a bin (the frequency) is of order and the relative standard error is of order . Compared to the next bin, the relative change of the frequency is of order provided that the derivative of the density is non-zero. These two are of the same order if is of order , so that is of order . This simple cubic root choice can also be applied to bins with non-constant widths.

Histogram and density function for a Gumbel distribution[23]


See also


  1. ^ Pearson, K. (1895). "Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 186: 343–414. Bibcode:1895RSPTA.186..343P. doi:10.1098/rsta.1895.0010.
  2. ^ Howitt, D.; Cramer, D. (2008). Introduction to Statistics in Psychology (Fourth ed.). Prentice Hall. ISBN 978-0-13-205161-3.
  3. ^ Naomi, Robbins. "A Histogram is NOT a Bar Chart". Forbes. Retrieved 31 July 2018.
  4. ^ M. Eileen Magnello (December 2006). "Karl Pearson and the Origins of Modern Statistics: An Elastician becomes a Statistician". The New Zealand Journal for the History and Philosophy of Science and Technology. 1 volume. OCLC 682200824.
  5. ^ "Histogram maker". histogram maker.
  6. ^ US 2000 census.
  7. ^ Dean, S., & Illowsky, B. (2009, February 19). Descriptive Statistics: Histogram. Retrieved from the Connexions Web site: http://cnx.org/content/m16298/1.11/
  8. ^ David W. Scott (December 2009). "Averaged shifted histogram". Wiley Interdisciplinary Reviews: Computational Statistics. 2 (2): 160–164. doi:10.1002/wics.54. S2CID 122986682.
  9. ^ a b c Scott, David W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. New York: John Wiley.
  10. ^ a b Sturges, H. A. (1926). "The choice of a class interval". Journal of the American Statistical Association. 21 (153): 65–66. doi:10.1080/01621459.1926.10502161. JSTOR 2965501.
  11. ^ e.g. § 5.6 "Density Estimation", W. N. Venables and B. D. Ripley, Modern Applied Statistics with S (2002), Springer, 4th edition. ISBN 0-387-95457-0.
  12. ^ "EXCEL Univariate: Histogram".
  13. ^ a b Scott, David W. (2009). "Sturges' rule". WIREs Computational Statistics. 1 (3): 303–306. doi:10.1002/wics.35. S2CID 197483064.
  14. ^ Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University (chapter 2 "Graphing Distributions", section "Histograms")
  15. ^ Doane DP (1976) Aesthetic frequency classification. American Statistician, 30: 181–183
  16. ^ Scott, David W. (1979). "On optimal and data-based histograms". Biometrika. 66 (3): 605–610. doi:10.1093/biomet/66.3.605.
  17. ^ Freedman, David; Diaconis, P. (1981). "On the histogram as a density estimator: L2 theory" (PDF). Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete. 57 (4): 453–476. CiteSeerX doi:10.1007/BF01025868. S2CID 14437088.
  18. ^ Wasserman, Larry (2004). All of Statistics. New York: Springer. p. 310. ISBN 978-1-4419-2322-6.
  19. ^ Stone, Charles J. (1984). "An asymptotically optimal histogram selection rule" (PDF). Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer.
  20. ^ Shimazaki, H.; Shinomoto, S. (2007). "A method for selecting the bin size of a time histogram". Neural Computation. 19 (6): 1503–1527. CiteSeerX doi:10.1162/neco.2007.19.6.1503. PMID 17444758. S2CID 7781236.
  21. ^ Jack Prins; Don McCormack; Di Michelson; Karen Horrell. "Chi-square goodness-of-fit test". NIST/SEMATECH e-Handbook of Statistical Methods. NIST/SEMATECH. p. Retrieved 29 March 2019.
  22. ^ Moore, David (1986). "3". In D'Agostino, Ralph; Stephens, Michael (eds.). Goodness-of-Fit Techniques. New York, NY, US: Marcel Dekker Inc. p. 70. ISBN 0-8247-7487-6.
  23. ^ A calculator for probability distributions and density functions
  24. ^ An illustration of histograms and probability density functions

Further reading