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Related Concept Videos

Review and Preview01:13

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
5-Number Summary01:04

5-Number Summary

In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
Boxplot01:12

Boxplot

Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
Modified Boxplots00:57

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which measures data spread, is...

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Related Experiment Video

Updated: Jun 21, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 3, 2011

The box plot: a simple visual method to interpret data.

D F Williamson1, R A Parker, J S Kendrick

  • 1Centers for Disease Control, Atlanta, Georgia.

Annals of Internal Medicine
|June 1, 1989
PubMed
Summary
This summary is machine-generated.

Box plots are a visual tool for exploratory data analysis, summarizing data distribution and identifying outliers. This method enhances understanding of complex tables and quantitative information.

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Area of Science:

  • Statistics
  • Data Visualization
  • Scientific Communication

Background:

  • Exploratory data analysis (EDA) uses statistical techniques to uncover patterns in numerical data.
  • Box plots offer a visual summary of data distribution, including median, quartiles, and range.
  • They are effective for comparing datasets and identifying outliers.

Purpose of the Study:

  • To demonstrate the utility of box plots in interpreting complex tabular data.
  • To advocate for increased use of box plots in scientific literature.

Main Methods:

  • Application of box plots to tabular data from two published articles.
  • Visual analysis of data distribution, spread, and symmetry using box plots.

Main Results:

  • Box plots effectively summarize and compare data groups.
  • The method aids in identifying outlier data values.
  • Visualizations improve the interpretation of complex tables.

Conclusions:

  • Box plots are valuable tools for enhancing reasoning about quantitative information.
  • The visual nature of box plots complements tabular data presentation.
  • Wider adoption of box plots is recommended for improved data interpretation.