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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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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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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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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Modified Boxplots00:57

Modified Boxplots

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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...
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Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

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In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
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Sea stack plots: Replacing bar charts with histograms.

Alice Dorothy Stuart1, Maja Ilić2, Benno I Simmons3

  • 1School of Environmental Sciences University of East Anglia, Norwich Research Park Norwich UK.

Ecology and Evolution
|April 18, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed sea stack plots, a novel data visualization tool, to accurately represent large datasets. This new method addresses limitations in existing plot types, improving data comprehension in scientific research.

Keywords:
bar chartsdata distributiondata visualisationhistogramssummary statistics

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Area of Science:

  • Data Visualization
  • Statistical Graphics
  • Scientific Communication

Background:

  • Graphs enhance data comprehension but poor design can mislead readers.
  • Existing plot types (e.g., boxplots, density plots) have limitations with large or unevenly distributed datasets.
  • Uninformative plots like bar charts and dot and whisker plots are prevalent in ecology and conservation literature.

Purpose of the Study:

  • To introduce a new plot type, the sea stack plot, for accurate and efficient representation of large univariate datasets.
  • To compare the effectiveness of sea stack plots against commonly used plot types.
  • To analyze current data visualization practices in ecology and conservation journals.

Main Methods:

  • Comparison of five common plot types (dot and whisker, boxplots, density, univariate scatter, dot plots) for data distribution representation.
  • Analysis of figures in four ecology and conservation journals to identify prevalent visualization methods.
  • Development of the sea stack plot and an accompanying R package ('seastackplot').

Main Results:

  • Commonly assessed plot types are difficult to read with large sample sizes or can misrepresent data distributions.
  • Bar charts and dot and whisker plots constitute 60% of univariate data panels for comparison in analyzed journals.
  • 16% of panels combined plot types (e.g., boxplots with density plots) for improved data display, indicating a need for better visualization tools.

Conclusions:

  • Sea stack plots offer an improved method for visualizing large and unevenly distributed univariate data, overcoming limitations of existing plots.
  • The development of user-friendly and accurate plot types like sea stack plots is needed to meet the demand for effective data visualization in scientific fields.
  • The 'seastackplot' R package provides a tool for researchers to implement this new visualization technique.