What do you do if you want to uncover biases and assumptions in Data Visualization using logical reasoning?

When addressing data visualizations or any descriptive statistics, my approach begins by identifying any outliers within the visualization. I assess whether these outliers are logically consistent with the business context. If they appear out of place, I proceed with caution towards the data visualization. I initially check for any errors at the data visualization layer. If none are found, the next step would be checking the data pipelines to ensure the business logic is accurately reflected in data collection. Finally, I would consult with the application or development teams to find out possible reasons behind any anomalies in the data.

Context is key when interpreting data visualizations. Let's say you stumble upon a chart comparing the performance of two competing smartphone brands. If you discover it was created by one of the brands themselves, you might suspect some bias in favor of that brand. Similarly, if a chart about climate change is produced by an environmental advocacy group, you'd want to scrutinize it for any potential exaggeration or cherry-picked data that supports their cause. It's like reading reviews online – you always check if they're from real customers or biased sources, right?

As an analytical observer, I start by questioning the origins of the data. Understanding the source of the data allows me to evaluate the reliability of the data visualization more effectively. If I detect any anomalies or inconsistencies, I investigate further into the data pipeline and consult with relevant teams to clarify and rectify these issues. This approach helps me make sure that the data visualizations are both- trustworthy and actionable for business leaders.

When looking at the design of a data visualization, you should check if it's easy to understand and whether it fairly shows what the data really says. A good design will make important data stand out. Here’s how you can make a visualization better: 1) Remove outliers: Sometimes, a few data points are way off from the rest and can mislead us by making the data look different than it really is. By removing these outliers, we make sure the visualization represents most of the data correctly. 2) Balance the dataset: If one part of your data has a lot more information than another, it might skew your results. Make sure each group is equally represented to reduce bias.

If visualization is made for larger audiences, simplifying data makes it easier to understand, but this can sometimes leave out important details. On the other hand, if it's made for decision-makers, they may know a lot already, and it's better to represent data more comprehensively and highlight important data points with key takeaways.

When reviewing data, using descriptive statistics helps spot patterns or issues that might suggest bias. For example, calculate the average, median, and most common value (mode) for a key performance indicator (KPI). Then, compare these to any outliers to see if they stand out significantly. This can indicate whether the data might be biased or not.