‘Accuracy vs Precision’ explains the key differences between these two often-confused concepts in data measurement. Accuracy refers to how close a measured value is to the true value, while precision indicates the consistency of repeated measurements. Both concepts are crucial, but their significance varies depending on the context, such as in scientific experiments, business analytics, or manufacturing. The article stresses that understanding the distinction can improve decision-making, especially when both accuracy and precision are essential for reliable outcomes.
What
As data professionals, we often need to differentiate between accuracy and precision. It feels like they allude to the same thing. But they represent quite different meanings, especially within an analytics context.
Accuracy refers to how close the measurements are to the ’true’ value, while precision refers to how close the measurements are to each other. In other words, accuracy describes the difference between the measurement and the actual value, while precision describes the variation you see when you measure the same part repeatedly. A good analogy for accuracy would be throwing darts. If a number of darts are thrown on a dartboard and if all of them centre around the bullseye, then the throws are accurate. Extending the darts analogy, if many darts were thrown at a dartboard and if they were in close proximity of one another, regardless of how far they were from the bullseye, they are precise.
Why
So, who cares and why does this actually matter? Understanding the true difference between accuracy and precision (and other related statistics) are vitally important when we evaluate machine learning models and AI systems. Especially, if they are mission or safety critical applications.
Let’s consider this for an example. Imagine an AI system that predicts presence of an illness. The vendor might claim a 99% accuracy of the system. But, what does this really mean? In order to fully understand this, we need to evaluate four statistical figures: accuracy, precision, recall and F1 score.
For this example, let’s assume the ‘99%’ figure was based on the AI system’s performance against the below test dataset.
• 100,000 cases in total
• 99,900 out of 100,000 cases are actually classified as having no illness (True Negatives)
• 100 out of 100,000 cases are actually classified as having the illness (True Positives)
The confusion matrix for this dataset could look something like this.
Based on this:
• Accuracy stands at 98.9%
• Precision stands at 8.2%
• Recall stands at 90%
• F1 Score stands at 15.0%
Based on above figures, can we conclude if this AI system is good? Should we invest in this AI system?
Well, it depends. It depends on what illness we are using the AI system to predict. What if those falsely predicted, but actual positive results (10) represent highly a contagious and infectious viral illness that can spread like wildfire? Or, what if that represent terrorists or a catastrophic threat assessment? All of a sudden, the system starts to look pretty dire in this context. Just one False Negative and the cost could be irrecoverable.
And this is the crucial point: context is key.
Remember: ‘accuracy’ is in the eye of the beholder, if we don’t use the right statistical figures to understand the complete picture.
How
This section outlines the key formulae used to calculate the above statistical figures.
Credit: Minitab Blog | https://lawtomated.com/
Kushan Kahadugoda is a seasoned business technology leader with a passion for driving transformative change through innovation. His experience spans diverse sectors — government, non-profit, education, telecommunications, financial services, and technology — and also includes Big Four consulting experience. Currently, he serves as Consulting Practice Director for AI, Innovation, and ESG at Oracle Corporation, leading initiatives in the ANZ region to foster innovation and enterprise transformation. Kushan is also a published author of 2 books.
More details: https://www.linkedin.com/in/kkahadugoda | https://kushan.blog/about
See Kushan’s profile here.