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Related Concept Videos

Scatter Plot01:15

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
Variation01:19

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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...
What is Variation?01:14

What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Related Experiment Video

Updated: Jun 26, 2026

Rapid Analysis and Exploration of Fluorescence Microscopy Images
11:41

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Variation in scatterplot displays.

Michael E Doherty1, Richard B Anderson2

  • 1Department of Psychology, Bowling Green State University, 43403, Bowling Green, OH. mdoher2@bgsu.edu.

Behavior Research Methods
|February 3, 2009
PubMed
Summary
This summary is machine-generated.

Scatterplot presentation significantly impacts data interpretation, yet standards are minimal. This study highlights arbitrary visual features affecting user perception and calls for clearer guidelines to improve data visualization consistency.

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Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Area of Science:

  • Data Visualization
  • Statistical Graphics
  • Human-Computer Interaction

Background:

  • Scatterplots are fundamental for visualizing variable associations.
  • Current publication standards for scatterplots are insufficient, leading to inconsistencies.
  • Arbitrary graphical features can distort user interpretation of data associations.

Purpose of the Study:

  • To investigate how non-associative features of scatterplots influence user interpretation.
  • To review empirical evidence on scatterplot design elements affecting inference.
  • To assess current scatterplot practices in scientific publications.

Main Methods:

  • Literature review of empirical studies on scatterplot perception.
  • Analysis of nine arbitrary scatterplot features influencing user inference.
  • Examination of 221 scatterplots from recent journal publications.

Main Results:

  • Numerous arbitrary scatterplot features demonstrably affect user interpretations.
  • Significant variation exists in the preparation of published scatterplots.
  • Existing literature provides recommendations for scatterplot construction.

Conclusions:

  • Standardization of scatterplot design is needed to ensure accurate data representation.
  • Flexible guidelines are proposed to mitigate the impact of arbitrary features.
  • Improved scatterplot practices will enhance the reliability of visual data analysis.