What are the most common mistakes made when creating graphical displays?

The selection of a visual representation hinges on the message you wish to communicate. Are you aiming to illustrate proportions, trends, or magnitudes? Is your data categorical or numerical? The appropriate mix of histograms, pie charts, or dot charts can simplify your communication. The key consideration is that the visual display should be more engaging and straightforward for the viewer than a table of numbers. It should not introduce more confusion than the original table for the viewer.

Choosing the right type of graphic for presenting any KPIs really, is crucial for effective communication. Different types of charts convey information in distinct ways, and the choice depends on the nature of your data and the message you want to convey. Imagine trying to convey a sales trend over months with a pie chart—it wouldn't be as effective as a line chart. So, the importance lies in aligning the visualization with the nature of the data to make it more understandable and impactful!

The art of choosing the right graphical display is akin to storytelling. Each type of graph or chart is a different narrative technique. Just as a skilled storyteller selects the right narrative style to convey a message, a data analyst must choose the appropriate graphical display that best tells the story of the data. For instance, using a pie chart to show time-series data is like trying to narrate a thriller in the style of a haiku – it's ineffective and confusing.

Graphical displays excel in simplifying complexity, making the case for the adage that simplicity is paramount in conveying information effectively. "Nothing is more simple than greatness; indeed, to be simple is to be great." Ralph Waldo Emerson

Choosing the wrong type of graph especially choose a pie chart to represent the annual sales over the five-year period. Why a line graph or a bar chart would be more appropriate for showing changes over time. Another mistake could be not labeling axes or data Points Clearly: Imagine the graph's axes are not labeled, making it unclear what the numbers represent. While the correct thing to do is to clearly label the X-axis with the years and the Y-axis with the sales amounts. Data points should also be labeled for clarity.

Simplicity is the soul of efficiency. Overloading a graphical display with too many elements is like a novelist who overuses adjectives and adverbs. It doesn't enhance the story; it buries it. A minimalist approach, focusing on what's essential, makes the data stand out, much like Hemingway's writing style. It's about striking the right balance between aesthetics and functionality.

For instance if you are displaying gdp per capita versus literacy levels, rather than annotating each country on the graph, you would just like to display average ones in terms of 4 combinational effects low gdp/low lit., low gdp/high lit. and so on and few outliers if necessary.

If you're telling a compelling story with your data, and each element is a character or plot point. Just as a well-crafted story is clear and engaging, a well-designed visualization should guide the viewer's attention to the main message without distractions. Each element should earn its place in the narrative, contributing to a cohesive and understandable whole.

Adding too many decorative elements or using overly complex chart types that distract from the data. While the correct thing to do is to stick to a simple and clean design that focuses on the data. Another common mistake is using Misleading Scales: where the Y-axis starts at a value higher than zero, which exaggerates the growth in sales. the right approach is to always start the Y-axis at zero to provide an accurate representation of the data.

The statement of less is more applies here. Keep graphics simple and clean.Also try not to use too many different colors. The less distractions you have the more the focus will be on your content.

Misusing scales and axes in a graph is similar to using a distorted lens in photography. It can dramatically alter the viewer's perception, leading to misinterpretations. It's crucial to choose the right scale, as it sets the stage for the data narrative. Just as a photographer selects a lens to capture a scene's essence, a data analyst must select the appropriate scale to accurately represent the data.

When working with scales and axes, we have to think of us as a curator of information. Our job is to present the data truthfully and transparently. We need to choose scales that reveal patterns without distorting reality. It's not about making the data fit a preconceived narrative but rather letting the data speak for itself, loud and clear. Accuracy in representation builds trust and ensures that your audience takes away the right insights from your visualizations.

Logarithmic scale sometimes may not be impactful as necessary for particular audiences, whereas due to extreme parity linear scale might not be proper. In those cases, simple treemaps etc may be useful.

Another thing to keep in mind with axes is that you are implying that changes in the X-axis variable are in some sense causing the changes in the Y-axis. There are exceptions like vertical timelines and economists usually putting price on the Y-axis, but in general audiences will have the subconscious take-away that X causes Y. If you have a legitimate outlier spike or bar that’s way out of scale with your other data points, have fun with it and incorporate it into your page layout.

Ignoring design principles in graphical displays is like ignoring grammar in writing. It may go unnoticed by some, but for those who are well-versed, it's glaringly obvious and detracts from the credibility of the work. Good design, like good grammar, provides clarity and enhances the viewer's understanding.

Ignoring design principles is critical! When creating graphical displays, you should consider yourself not just a data analyst but also a visual storyteller. Embrace design principles as your allies in crafting a narrative that captivates and enlightens your audience. After all, a well-designed visualization isn't just about data; it's about creating an experience that resonates with the viewer.

Using bright, clashing colors that make the graph hard to read. Better still, use a color scheme that is easy on the eyes and provides good contrast for readability. Another common mistake using graph that's stretched in a way that distorts the data representation. Correction: Maintain an aspect ratio that accurately represents the data without distortion.

Failing to check for errors in a graphical display is akin to publishing a book without proofreading. It risks the integrity of the content and can lead to embarrassing mistakes. Just as a proofreader ensures the quality of a manuscript, thorough checking of a graphical display ensures the accuracy and reliability of the data presented.

You have to be careful first of all! Before you unveil your visualization to the world, take the time to review and validate every element. Cross-check data, verify calculations, and scrutinize labels. It's not just about avoiding embarrassment; it's about upholding the integrity of the information you present. Always remember, a well-checked visualization not only communicates data effectively but also builds confidence in the reliability of your insights!!

In addition it is important to note if the question allowed for multiple responses ( figures can add to more than 100%) or single responses ( should add up to 100%)

Not Providing Context or Explanatory Text: The graph is presented without any context or explanation about what it represents. Correction: Include a brief title and, if necessary, a subtitle that explains what the graph shows. You may also add a short description if the data needs further context.

Not testing a graphical display for clarity is like a playwright not conducting a dress rehearsal. It's a missed opportunity to see how the audience reacts and make necessary adjustments. Testing for clarity ensures that the graphical display effectively communicates the intended message and resonates with the audience.

One common mistake is clutter! In other words, you should always avoid to to show too much information into one display! Based on my past experience, it might end up messy and confusing as such my advise to future data people is to always keep it as simple as possible. Another pitfall is using overly complex charts or graphs when a simpler option would do the trick. Think of it like using a sledgehammer to crack a nut—effective, but a bit much. Lastly, neglecting to label or provide context for your visual elements can lead to a serious case of confusion. Imagine a treasure map without any key—it'd be tough to find that "X marks the spot." Always make sure your graphics speak a language everyone can understand!

In my experience this is all too common in academia, I am thinking of PPT slides and poster presentations. Other issues include clashing color combinations or issues with legibility due to background and font sharing colors.

By avoiding these common mistakes, you ensure that your graphical display accurately and effectively communicates the intended information, helping viewers to correctly interpret and understand the data.

I would give you some real company examples that can give you a prospective here... One infamous example is the London 2012 Olympics logo. Many found it confusing and even compared it to a distorted swastika. The design faced widespread criticism for its lack of clarity and aesthetic appeal. In the realm of charts and graphs, Microsoft's Excel has been unintentionally misused by several organizations. For instance, in 2013, the American University in Washington, D.C., sent out a chart to prospective students that had some y-axis manipulation, making the job placement rates look much higher than they actually were. As such I would say - avoid unintended confusion or misinterpretation as much as you can!

There is this tendency to use full spectrum for heatmapping scalar values, as they appear more "beautiful". But at the expense of proper interpretability. Monochromatic shades should be go-to choice.