What are the most common statistical graph pitfalls?

It is utmost important to select the appropriate graph as per the data type to understand it. If we select the wrong graph, there will be high chances that we will get wrong patterns or we won’t get any patterns and which will lead to different spurious relations between the data points. For example, if we have percentages or share it’s better to go with pie chart and if we have a time-series data then line chart will be a better option.

In data visualization, a crucial initial step is selecting the appropriate graph type aligned with your data and objectives. Different graphs possess distinct strengths and weaknesses, and an incorrect choice can result in misleading or irrelevant outcomes. For instance, when comparing proportions or percentages, a pie chart might seem fitting, but accurately comparing angles and sizes can be challenging. Opting for a bar chart or stacked bar chart could provide better clarity. Similarly, for illustrating trends over time, a line chart proves more suitable compared to a potentially cluttered and confusing scatter plot. The key lies in aligning the chosen graph type and the intended purpose to ensure accurate and meaningful visualization.

As part of my actual experience using the big data analytics. The use of graphics should be assure the follow: - Visualisation Visualisations allow different lenses to be applied to the planning data providing for deeper insights and faster decision-making. - Integration Integration of business information and data unlocks the efficiency that allows our descriptive reporting to be more predictive. - Simulation Simulations allow us to explore ‘what if’ questions and scenarios, which provide insights into market uncertainty, allowing us to make the best planning decisions. Best regards.

I think this is and honestly will be the meat of the matter here. You can do a bunch of different optimizations (as the ones listed below) but if you have a graph that's incompatible or incapable to share the message you are trying to convey, that's a DOA right there. So before you choose a graph, do this simple thought experiment. Ask yourself what is the punchline the audience needs to walk away with. Is it a comparison? Is it a distribution? What do you want your audience to know? Once you have that, narrow your choices, there will be a couple of graphs that can serve the purpose. Pick the one that is best suited for the data type. And then follow the steps below. And you are all set!

People tend to include predictive graphs to visualisation as there are inbuilt easy configurations readily available. Many a times the contributing factors are not properly mapped, leading to useless or misleading outcomes.

In my experience, it is the truncated axis that gets analytics professionals in trouble. Especially when there are multiple charts on a slide. By narrowing the range of the axis, magnitudes of the smallest amount can seem significant to the user. It’s like using a magnifying glass on the differences which will distort the data story on what is meaningful. This issue is exacerbated when you have a large range on one chart and small range on the other. I would recommend having identical scales on the charts on the same page to avoid this issue. People’s mind automatically see the charts this way, so you should avoid confusion and choose the simple data visual design choice here.

Having a secondary axis with a substantially different scale than the primary axes in a graph can introduce a form of distortion that influences the perceived correlation between two metrics. This practice is often seen in line charts or scatter plots where two variables with different units or magnitudes are displayed together. If the scales of the primary and secondary axes are not carefully chosen, the visual correlation between the two variables can be distorted. If the scales are substantially different, the correlation may appear stronger or weaker than it actually is. Carefully select the scales to accurately represent the magnitude of the data.

A common mistake in data visualization is employing inappropriate scales and axes, impacting clarity, accuracy, and data interpretation. For ex: opting for a logarithmic scale over a linear one may lead to exaggerated or understated value differences. A truncated axis, as opposed to a full one, can create a false impression of trend magnitude or direction. Using a dual axis instead of a single may imply a correlation or causation that isn't real. To sidestep these issues, it's crucial to select scales and axes tailored to the data type, range, and distribution, ensuring they accurately represent rather than distort or obscure the data. Matching these elements to the nature of the data is essential for effective and truthful visualization.

Agree with this. It is very usefull for the person who is new in this field Using inappropriate scales and axes can be a significant pitfall in creating statistical graphs. This includes misrepresenting data by manipulating the scale to exaggerate differences or downplay variations. It's crucial to choose scales that accurately reflect the magnitude of the data and to clearly label axes to avoid misleading interpretations. Misleading scaling can distort the perception of trends and patterns in the data.

De la misma manera que una buena gráfica puede ayudar a tu audiencia a comprender mejor tu mensaje, una mala gráfica puede facilitar el erróneo entendimiento del mismo. En el ámbito profesional, comenzar mis escalas desde cero y mantener el mismo eje al realizar comparaciones de gráficas de barras me ha ayudado a disminuir las malinterpretaciones. De igual manera, logro una mejor interpretación de la información al exponerla. Por ejemplo, si colocas una gráfica de barras sobre las ventas de tu empresa justo al lado de la gráfica de barras de las utilidades, si no tienes el cuidado de mantener al mismo nivel tus ejes X, es probable que las utilidades se interpreten casi al mismo nivel que tus ventas.

White space is your friend. Especially in longer form infographics and data visualizations, remember that you don’t have to crowd every data point into the visualizations. Make sure you are bringing the most compelling data that supports that overall narrative that you are portraying.

Mantén tus visualizaciones simples. ?Qué es lo que quieres comunicar? El tiempo de atención de la audiencia es mínimo; utiliza colores o formas dentro de tu visualización para captar la atención hacia tus puntos de interés y sé conciso. La elegancia en la simplicidad de una gráfica es altamente valorada en las mesas directivas.

A common error in data visualization is introducing unnecessary clutter and noise, diminishing readability, impact, and audience engagement. Excessive use of colors, fonts, labels, legends, grid lines, or other elements can distract from the main message or cause confusion. Overloading with irrelevant or unreliable data may obscure key insights or introduce errors. To avoid these pitfalls, adhere to the principle of simplicity, prioritizing essential data and information. Utilize visual cues like contrast, alignment and hierarchy to guide the eye and emphasize crucial points. By focusing on clarity and simplicity, you enhance the efficacy of your visualization, ensuring a more impactful and comprehensible presentation for your audience.

I'm lucky enough to work in an environment where I: build, advise others, see and get feedback on 100s of dashboards. From the new joiner out of college, all the way to global business leaders and everyone inbetween. And the most common complaint I hear is: " there is just to much stuff here! I'm not using this!" This could be the amount of colours, 7 tables on one page, different sizes of writing, the misalignment of elements or all of the above at once. In the realm of visualizations, less is more. We are trying to convey relevant information and insights to easily navigate complex data. So take the time to understand the true needs of the users, as well as invest the time to understand interface design principles.

Data ink ratio is a concept everyone needs to know. I think showing less is always the best. Also, whitespace helps you to show and communicate precise

Neglecting ethical and cultural considerations in the creation and presentation of graphs is a common pitfall that can jeopardize the credibility and trustworthiness of your visualization. Employing misleading or manipulative techniques, such as cherry-picking or altering data, violates principles of honesty and transparency. Additionally, using offensive symbols, colors, or words may alienate certain groups. To avoid these pitfalls, it is crucial to adhere to data visualization ethics and best practices, considering the standards and guidelines. Tailoring your visualization to align with the context and expectations of your audience is essential to ensure sensitivity, respect, and the ethical integrity of your visual representation.

Neglecting feedback and evaluation is a common pitfall that can compromise the quality and effectiveness of your visualization, hindering skill improvement. Failing to test graphs with the intended audience or experts may result in overlooked errors or flaws. Without measuring the impact or outcome, gauging goal achievement becomes challenging. To avoid these pitfalls, actively seek and integrate feedback on your graphs. Utilize tools like usability testing, eye tracking, surveys, or analytics to assess and optimize your visualizations. Incorporating feedback and employing evaluation methods is essential for refining skills, ensuring the quality of your visualization, and achieving intended goals.

Importantly, make sure to seek feedback on your visualizations from others who are free from bias and who do have anything to gain or to lose from the apparent message of the graphic. Naturally, a more balanced and objective visual will promote greater credibility long-term. Otherwise, an echo chamber is created and amplified.

La retroalimentación muchas veces es olvidada cuando nos referimos a la analítica de datos, porque muchas veces el usuario prefiere descargar los datos para poder realizar sus propios análisis privando al equipo de contar con un feedback para mejorar las visualizaciones, cálculos de ratios y funcionalidades de análisis que se usan, a veces tener un sistema funcionando de manera correcta simplemente hace que el feedback quede de lado.

As much as visualization is the presentation of information, there is definitely a creative spirit to it. Try out various designs, take the same visualization and try create it in a variety of formats, methods, layouts and colours and over time and various iterations, you'll slowly find yourself re-using colours, elements and layouts that eventually contribute to your style of creating visualizations and telling the data story your way. I often compare visualization style to an ecosystem as well. A lion thrives on the open plains, but struggles in the waters. Just because something works for someone else, doesn't mean it works within your visualtion ecosystem and visa versa.

Aunque como analistas solo somos un medio utilizado por los datos para expresarse, tienes la libertad de comunicar tus mensajes de la manera que consideres más efectiva para tu audiencia. Evita utilizar un lenguaje con el que no estés familiarizado y no te pongas en situaciones que puedan distraerte al exponer resultados. Puedes elegir la estructura o metodología que mejor te funcione, mencionar las emociones que experimentaste al descubrir algo relevante o proporcionar el contexto del proyecto desde tu perspectiva personal. Este aspecto es único e irrepetible, algo que solo tú puedes comunicar

Surprisingly I’ve not seen mentioned data quality yet. No matter how well you visualise your data, if the quality is lacking you’ll lose all credibility in what you’re trying to convey. Spend the time to cleanse and refine your data set. Think about what you’re trying to present/convey when doing so. Do you want to exclude outliers/poor data or highlight it? Have you picked the correct table joins on large datasets? Have you dealt with duplicate, incomplete data or other errors?

The first thing that must be considered before choosing the type of graph and the scales is the purpose of the statistical information we want to communicate and the end user who is expected to make use of it. In addition, most of us tend to forget that the statistical information has a spread and the values are not as sharp as they appear on the graph. This has an immense effect on the practical part of it. The process of collecting data for each graph should be discussed in detail with the end user and the standard deviation of the data or the interplay of factors beyond control should be clearly considered.

Let's put aside the data and development of visualizations. I see it over and over again internally and at my clients that visualizations are built, without taking the users needs into account, which ultimately results in more time rebuilding elements, which can cause tension between the builders and the users. Apart from data, build and design errors, I believe the number one pitfall is not bringing the users of the visualizations along the journey, from start to end. Yes, it is definitely more time consuming to have all the additional calls and design sessions throughout the process, but at the end of the day, most visualizations we build are for others, not ourselves. So always remember to take the users on the journey to.

Always check your work in data visualization graphs. I’ve seen endless examples where you can easily see that the graphs and charts and up equaling more than 100%. It could have happened from a typo in a data table and is easy to lose credibility to your audience.

Failing to consider key aspects of data visualization such as highlighting and comparing values. For example, using a bar or a column chart and failing to order the chart according to the measure of interest may make it harder to communicate the intended message correctly. Inappropriate use of colors is another key one, for example, it would be inappropriate to use a red color to depict positive growth in a key measure of interest. Finally consider using an enclosure, different colors, different shapes, or a pie position to highlight the key value you want your audience to focus on.