Okay, so you finished your data analysis and found that there were statistically significant differences. You then ran a posthoc test and found exactly where these differences were. Now what? Graph time!
Choosing a graph or chart that best suits your data can be difficult at times. You can’t just slap on a whole bunch of points and call it a graph. Your graph needs to show your findings in a crisp, clean and effective way. When I was making my figures and tables for my thesis, I had to go back to the drawing board many times until I was able to find the right figure.
This guide will go over how to select the right figure, and how to troubleshoot problems that may arise when using Julius to help create them.
Types of Data Visualizations

Bar Chart: these are typically used when comparing values of different categories or groups.
Example: monthly sales performance of different products 
Line Chart: these are ideal for showing trends over time or continuous data (data that can take on any value and can change over time).
Example: temperature variation over a week in a particular city, with each point specifying the temperature at a specific time. 
Pie Chart: these are useful for displaying parts of a whole. These are not as popular anymore in some scientific articles or publication forums, well from my experience anyways…
Example: displaying the distribution of a company’s quarterly expenses, with each “slice” showing a proportion of the total expenses. 
Histogram: these are effective when displaying distribution of continuous data. Typically used when doing exploratory analysis on your dataset when you want to see the skew, normality and kurtosis.
Example: a histogram depicting the distribution of ages in a population sample. 
Scatterplot: these are used for visualizing the relationship between two continuous variables.
Example: a scatterplot that plots the relationship between study hours and exam scores for a group of students. Each point on the plot is a student, with their study hours plotted on the xaxis and their exam scores plotted on the yaxis. 
Box Plots (my favourite!): helpful for displaying the distribution of datasets and highlighting the outliers in them. Typically used when doing exploratory analysis on your dataset to visualize skew and outliers.
Example: showing the distribution of salaries in a company; provides minimum, maximum, median, and quartiles of the dataset. Additionally, provides information on the central tendency and variability of salaries. 
Heatmap: these are very good at displaying patterns and relationships in large datasets (i.e. multidimensional data).
Example: visualization of the correlation matrix of stock prices for portfolio of assets. Each cell in the map indicates a correlation coefficient between two of the stocks, with the warmer colours (red) representing stronger positive correlations and cooler colours (blue) representing strong negative correlations. 
Polar Plots (also a personal favourite): this graph uses polar coordinates to represent data. Polar coordinates use radial axis and angular axis.
Example: mapping animal movement patterns to see if they frequent a specific direction more than others. Analyzing circular distributions of data.
Troubleshooting Graphs on Julius
Question 1a: I want Julius to create a simple bar graph of the monthly sales performance on different products.
Prompt 1a: can you create a simple bar graph that displays the monthly sales performance of different products?
I prompted incorrectly: this was not the graph I wanted it to make. Although it is very crisp and clean, I want a simple bar graph, not a stacked one. Let’s try prompting it again but be more specific:
Prompt 1b: taking the data from this chart, can you create a simple bar graph so that each individual bar is a product, and it shows the monthly sales performance across one year?
So, this still was not what I had in mind, however, it is something closer to what I wanted to display. I’m going to prompt it again and see if I can get to create what I want.
Prompt 1c: can you create a grouped bar chart illustrating the mean monthly sales performance of different products (Product A, Product B, Product C, and Product D) over a year, with four bars for each month?
This is the bar graph I had in mind; however, it looks very messy and would probably not be a good visual to show the mean annual sales over time between the four products. The bar graph from Prompt 1b would probably the one to use to show how each product’s annual mean sales compare to one another. However, I want to see how the sales change over time. So, let’s try another visualization.
Prompt 2a: can you create a line graph showing the trend of total sales over the year?
Julius did create a graph showing the overall trend of sales over the year, but I wanted to see it between all four products. I again prompted incorrectly, so let’s try again:
This is what I had in mind! However, it is still very messy and distracting. Let’s ask Julius to create separate graphs for each product:
Prompt 2b: can you create a line graph for each product please?
Almost done! I just want to make each graph a different colour and remove the headings and gridlines to create a crisper look.
Prompt 2c: now you make product A blue, product B orange, product C green and product D red please? As well as remove the headings and gride lines for each graph please?
I would separate these two sentences into different prompts, but for the guide I placed them together
Now we just need a legend! We can edit the graph with the “edit graph” button that Julius shows whenever you create a visualization to place a legend on the graph. However, if the graph editor is not showing the legend when you click on it, try prompting Julius to place the legend on the graph itself.
Final Note: You should have standard error bars or standard deviations on your graphs to show the variability of your dataset. Since I had Julius make up this dataset, when I prompted to ask to place the standard errors on my points it did not show up. Your dataset should however have these (hopefully), and I encourage you to place them on any graph you make.
Happy graphing!
Keywords: AI, GPT 4, Claude 3, Data Analysis, Data Science, AI, Statistics, Statistical Analysis, Data Visualization, Graph Type, Charts