Accuracy vs Precision: Differences, Examples, and Use

Accuracy and precision are often used interchangeably in normal life. But in terms of measurement, they're defined differently. Just because a measurement is accurate does not mean it's precise, and vice versa. Both accuracy and precision are critical to producing high-quality work, but what are they? Let's take a look at the differences and when and how to use each one.

What is the difference between accuracy and precision?

Accuracy measures how close a result is to the true or target value. Precision measures how close repeated results are to each other. You can be accurate without being precise, precise without being accurate, or both.

Accuracy vs. precision: the bullseye example

The bullseye example is the most common way to show the difference between accuracy and precision. Think of throwing darts at a bullseye; the goal is to be both accurate and precise.

• Accurate only: Your darts land close to the bullseye, but not consistently in the same spot

• Precise only: Your darts land close to one another, but not necessarily close to the bullseye

• Both accurate and precise: Your darts land in the bullseye every time, the best-case scenario

• Neither: Your darts are scattered across the board, far from the bullseye and far from each other

What is accuracy?

Accuracy measures how close your result is to the actual value you were trying to achieve. Whether that's a strategic goal or a personal win, accuracy tells you how closely you hit what you're aiming for. You can determine accuracy after a single event.

• High accuracy: You land directly on your target value

• Low accuracy: You're way off the mark

Let's look at an example. You've set a KPI to reduce your site's bounce rate by 12% in this upcoming fiscal year. If your measurement shows you hit exactly 12% by the end of your fiscal year, you've achieved 100% accuracy.

What is precision?

Precision measures how close your results are to one another. Unlike accuracy, precision requires multiple measurements over time to determine the degree of closeness between each result.

• High precision: Your results are similar to each other

• Low precision: Your results are all over the map

Measuring precision is helpful in two scenarios:

• When you're trying to avoid making the same mistake

• When you're achieving successful results and want to establish a process for reproducibility

Using the above example, let's say all your pages'bounce rates were reduced by the same percentage. So every page on your site had its bounce rate decrease by the same amount, whether that's 6% or 20%. This is highly precise, even though it's not accurate.

How do you track and measure accuracy and precision?

To know if you are accurate or precise, you'll need to track and measure your results. Accuracy and precision are measured differently:

• Accuracy measurement: How your result compares to the target value. The closer you are, the more accurate you are. To determine accuracy, you need to have clearly defined goals, project baselines, or success metrics to aim for.

• Precision measurement: How close measurements are to each other. To review for precision, you'll need to develop a tracking system that shows how multiple results or data points compare over time.

When it comes to a measurement system, you'll want something that's easy to manage and accurate. A project management software with universal reporting can help you track, and react to, results in real-time. For example, if you notice consistent results (precise) moving away from your target goal (inaccurate), you may have a systematic error that needs correction.

Examples of accuracy and precision

Just about everything you read about accuracy and precision will give you the bullseye example used above. But let's look at some more practical examples of how you would use accuracy and precision in your day-to-day life.

Example 1: Building a shared database

Here's the scenario: your team has felt scattered recently, so you decide to create a shared database where everyone has access to data and reporting in one, centralized space. You hope that if they're connected to all information in real-time, it will improve collaboration long-term.

Here are the four scenarios for how this would go, depending on how accurate and precise your database is:

1. Accurate: If you're highly accurate but not as precise, you'll build the database, but you might only see collaboration increase in one or two projects.

2. Precise: If you're only precise, you might add many similar items to the database, but they might not be the ones your team needs to collaborate.

3. Neither: You try to build a database, but you're the only one who can access it, which doesn't encourage collaboration.

4. Both accurate and precise: You build a shared database with a multitude of projects that are easy to collaborate on as a team. The system you use is repeatable, so you can continue to collaborate on new projects as they come up.

Example 2: Improve response rate

Your team sets a goal to respond to customer tickets within 48 hours. The scenarios could be:

1. Accurate: Some tickets are responded to within 48 hours, but some aren't.

2. Precise: All tickets are responded to within the same timeframe, but not within 48 hours.

3. Accurate and precise: All tickets are responded to within 48 hours.

4. Neither: No improvement or ticket times get slower.

Example 3: Dressing for work

Let's look at a simpler, day-to-day example. How do you get dressed for work?

To start, you check the weather report the night before so you can lay out clothes. It's supposed to rain so you decide to wear waterproof boots and a raincoat, plus bring an umbrella. If it rains, boom, that's your validation. Your outfit was accurate.

If it doesn't rain, the outfit is still precise; these items are all related to each other, but they're not accurate. Instead, you're sweating in your too-heavy clothes and lugging around an unnecessary umbrella.

Accuracy vs precision: Which is more important?

So which one do you need more of, accuracy or precision? Like many things in project management, the answer is, "it depends." To achieve strategic initiatives, you'll want both. But if you have limited resources or time, you might not be able to guarantee accurate, precise results all the time. In this case, focus on the accuracy of a measurement first.

Keep in mind that precision is important for follow-up. Continuous improvement through varying techniques helps you refine your results, becoming more precise as you test.

When accuracy is more important

Accuracy matters most when the actual value or right answer is critical. Here are two examples:

• Medical diagnosis: A doctor must accurately identify a patient's condition to provide appropriate treatment. Being precise but inaccurate could lead to consistently misdiagnosing patients with serious consequences.

• GPS navigation: An accurately calibrated GPS unit will guide you to the correct destination, even if the exact route varies. Reaching the correct location matters more than precise directions.

When precision is more important

Precision becomes more critical in contexts where consistency and repeatability are key. Here are two examples:

• Manufacturing: When producing smartphone components, each part must fit perfectly with the others. High precision ensures every piece is nearly identical, allowing for smooth assembly. In this case, consistency between parts is often more critical than overall accuracy.

• Competitive sports: A darts player who consistently hits the same spot, even if it's not the bullseye, can adjust their aim more effectively than one who hits the bullseye occasionally but is inconsistent overall.

Accuracy vs precision: How to measure and improve both

Understanding the difference between accuracy and precision enables informed decision-making and process optimization. Here's how companies can measure and enhance both precision and accuracy.

4 techniques for improving accuracy

To ensure your measurements and results are as close to the real value as possible, try these proven strategies:

• Data cleaning and validation: Implement rigorous data cleaning processes. Financial analysts might cross-reference multiple data sources to validate revenue figures against actual earnings.

• Bias reduction: Employ methods to reduce bias in market research. This might involve using diverse sampling techniques for a more accurate representation of the target market.

• Calibration: Regularly calibrate sales forecasting models by benchmarking against historical data. This process aligns forecasts more closely with actual outcomes.

• Error analysis: Conduct a root cause analysis to find the origins of inaccuracies. This involves scrutinizing assumptions and inputs in your models.

4 techniques for improving precision

Achieving consistent and reproducible results is key to precision. Here are some effective techniques:

• Standardized operating procedures: Develop standardized operating procedures to ensure consistency across business processes, particularly in manufacturing, where repeatable processes are essential.

• Continuous monitoring and adjustment: Implement systems for continuous monitoring and adjustment of business processes. Use control charts to track variability in production quality over time.

• Track key performance indicators (KPIs): Track detailed key performance indicators (KPIs) to detect subtle performance shifts. Granular metrics like sales velocity and customer acquisition cost reveal trend changes that broader metrics miss.

• Repeated sampling in quality control: Perform repeated sampling in quality control processes over short time periods. This technique ensures products meet precise specifications.

Tools and technologies for measurement

Leverage advanced tools to achieve high levels of both accuracy and precision in measurements and decision-making processes. Here are some of the key tools and technologies companies use:

• Business intelligence dashboards: Utilize BI dashboards for precise data visualization and trend analysis. These tools often feature quadrant analyses to identify high-performing areas and areas needing improvement.

• Predictive analytics software: Employ predictive analytics for high-precision forecasting and risk assessment. These tools use machine learning algorithms to process vast amounts of data and identify patterns.

• Customer relationship management (CRM) systems: Use them to track customer interactions and segment the customer base. These systems help businesses maintain detailed records of every customer touchpoint.

• Enterprise resource planning (ERP) systems: Implement ERP systems to track business resources and support financial reporting. These comprehensive systems ensure that all parts of a business work with the same data.

Accuracy vs precision: Real-life business applications

Striking the right balance between accuracy and precision is important across numerous business scenarios. Understanding this interplay can significantly improve decision-making, quality control, and operational efficiency in your measurement process.

In market research and product development

The success of products and services relies on balancing accuracy and precision in market research and product development. Let's explore the methods to achieve this balance:

• Consumer preference analysis: Balance survey design precision with accuracy in representing consumer preferences. Researchers must craft questions that capture consumer sentiment using Likert scales where neutral responses are as important as the extremes.

• Product testing and iteration: Ensure high accuracy in identifying product issues while maintaining precision in improvement iterations. Product teams might use A/B testing to gauge user preferences and adjust features.

• Pricing strategy optimization: Use market data and pricing models to optimize product pricing. This might involve analyzing price elasticity to determine the most profitable price points.

In operations and supply chain management

Operations and supply chain management require attention to both accuracy and precision. Here are some strategies to optimize both:

• Inventory management: Use inventory management systems that provide stock levels and demand forecasting. This might involve RFID technology for real-time tracking, combined with AI for predicting future needs.

• Logistics optimization: Implement logistics software to track shipments and optimize delivery routes. This might involve GPS tracking for location data and algorithms that calculate efficient routes.

• Lean manufacturing: Implement lean manufacturing principles that identify waste and measure process improvements. This often involves value stream mapping to visualize the production process and target areas for enhancement.

Achieve both precision and accuracy in your work

At the end of the day, you'll need both accuracy and precision to complete your best work. You might develop the skills for precision over time and keep missing your target, until one day it falls into place. Suddenly, your precision is landing you on your target goal over and over again.

To reduce errors and boost productivity, it might be time to get some help. Project management tools track, report, and measure your results for you. Get started to streamline your work and achieve more accurate, precise results.

Frequently asked questions about accuracy and precision