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Related Concept Videos

Bar Graph01:07

Bar Graph

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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...
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Multiple Bar Graph01:07

Multiple Bar Graph

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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...
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Time-Series Graph00:54

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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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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Review and Preview01:13

Review and Preview

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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...
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Related Experiment Video

Updated: Sep 5, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
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Replacing bar graphs of continuous data with more informative graphics: are we making progress?

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  • 1QUEST Center for Responsible Research, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany.

Clinical Science (London, England : 1979)
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Scientists often misuse bar graphs for continuous data. While some fields improved visualization practices, many still need better data representation, especially in biology and medicine.

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Area of Science:

  • Scientific visualization
  • Meta-research
  • Data presentation

Background:

  • Bar graphs are frequently used to display continuous data, which can obscure data distribution.
  • Awareness of the need for improved data visualization practices has grown.
  • The impact of these awareness campaigns on scientific visualization practices remains unknown.

Purpose of the Study:

  • To examine the frequency of various graph types used by scientists across different fields.
  • To assess changes in scientific visualization practices between 2010 and 2020.
  • To identify fields with high rates of incorrect bar graph usage.

Main Methods:

  • Developed and validated an automated tool to screen scientific papers for specific graph types.
  • Randomly selected approximately 1000 papers per year from 23 fields in PubMed Central (n=227,998).
  • Analyzed graph usage and identified trends in visualization practices over a decade.

Main Results:

  • Bar graphs are more often misused for continuous data than correctly used for counts or proportions.
  • The proportion of papers using bar graphs for continuous data varied significantly by field in 2020 (4-58%).
  • High rates of continuous data bar graph usage were observed in biochemistry, cell biology, complementary and alternative medicine, physiology, genetics, oncology, pharmacology, and microbiology.

Conclusions:

  • Scientific visualization practices have improved in some fields, but widespread issues with bar graph usage persist.
  • Flow chart adoption remains low (<25%), limiting information on attrition and bias.
  • Targeted interventions are needed to improve data visualization, particularly in fields with high rates of incorrect bar graph usage.