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Review and Preview

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 Preview

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...

Bar Graph

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...

Multiple Bar Graph

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

Pie Chart

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

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Related Experiment Video

Updated: Jul 5, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Graphical data presentation.

Dirk Stengel¹, Georgio M Calori, Peter V Giannoudis

¹Centre for Clinical Research, Department of Orthopaedic and Trauma Surgery, Unfallkrankenhaus Berlin, Warener Str. 7, 12683 Berlin, Germany. dirk.stengel@ukb.de

|May 27, 2008

Summary

This summary is machine-generated.

Effective scientific figures maximize data density and minimize chartjunk for clear communication. Essential graphical tools include histograms, bar charts, box-and-whiskers plots, scatter plots, and forest plots for impactful data visualization.

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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

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Area of Science:

Scientific communication
Data visualization
Research methodology

Background:

Figures and charts are critical for disseminating scientific information and influencing manuscript acceptance.
Graphical excellence enhances the visibility and impact of study results within the scientific community.

Purpose of the Study:

To define the key characteristics of excellent scientific graphics.
To outline essential graphical tools for researchers.

Main Methods:

Defining graphical excellence based on data density, ink-to-data ratio, and clear axis labeling.
Identifying a core set of recommended chart types for scientific reporting.

Main Results:

Graphical excellence is characterized by high data density and a low ink-to-data ratio, avoiding 'chartjunk'.
Clear and unequivocal axis labeling is crucial for effective data representation.

Conclusions:

Researchers should prioritize data density and minimize extraneous elements in figures.
A foundational toolbox of specific chart types (histograms, bar charts with error measures, box-and-whiskers plots, scatter plots, forest plots) is recommended for robust scientific visualization.