Data visualization methods can summarize the results of your research in a graphical summary for easy understanding.
Here are some of the best data visualization techniques for common statistical tests:
1) Chi-square test or Fisher exact test:
For tests where both the variables are categorical like chi-square test and fisher-exact test, bar-charts are most commonly used.
For example: Let us visualize percentage of vegetarian and non-vegetarian in a class of 70 students according to gender
For Pearson correlation, where both the variables are continuous, scatter plot with regression line is best to visualize the data.
For example: Let us visualize height (cm) in X-axis and weight (kg) in Y-axis in a class of 24 students
For Spearman correlation which can also be used for ordinal data and also as a non-parametric alternative of Pearson correlation, scatter plot can be used without a regression line for visualization.
3) t-test or One-way ANOVA:
Since t-test (independent t-test and paired t-test) and one-way ANOVA are used to assess the mean (± SD) difference between the groups, plot with error bars is the best for representing these tests. Box-plot is also commonly used for visualizing t-test and one-way ANOVA.
For example: Let us visualize mean ± standard deviation of height of students in a class of 24 students according to gender
4) Mann-Whitney U test or paired samples Wilcoxon test or Kruskal-Wallis test:
Mann-Whitney test, paired samples Wilcoxon test and Kruskal-Wallis test are non-parametric alternatives of independent t-test, paired t-test and one-way ANOVA respectively. In these tests, variables are usually expressed as median (interquartile range); therefore boxplots are the best data visualization technique for these tests. In the box-plot, we can visualize median, interquartile range (i.e. 1st quartile and 3rd quartile), minimum and maximum. Since non-parametric tests like Mann-Whitney test, paired samples Wilcoxon test and Kruskal-Wallis test are used to assess difference in spread of variables between groups, box-plot is the best option.
For example: Let us visualize spread of weight of students in a class of 24 students according to gender
5) Time-series analysis:
Line chart is the simplest way to visualize time-series data. Stacked area chart and bar chart are the next two best options.
For example: Let us take an example of IMR (per 1000 live births) in India from 2010 to 2019
- Agresti A. An introduction to categorical data analysis. 2nd ed.: A John Wiley & Sons, Inc., Publication; 2007.
- Daniel W.W., Biostatistics: A foundation for Analysis in the Health Sciences. 9th edition: A John Wiley & Sons, Inc., Publication; 2009.