PRECISION, TRUENESS, AND ACCURACY: WHY MEASUREMENT MATTERS IN A DATA-DRIVEN WORLD

Explore the real difference between precision, trueness, and accuracy—and learn how mastering these concepts can drive better decisions in work, learning, and life.

Why Get It Right?

In a world shaped by data and driven by decisions, the ability to measure correctly isn’t just helpful — it’s essential. Whether you're a data analyst, quality control manager, engineer, or everyday problem-solver, understanding the difference between precision, trueness, and accuracy empowers you to make better choices, reduce errors, and build trust. Yet, these terms are often misused or misunderstood.

So what do they really mean? Why does it matter whether your results are precise or accurate? And how can we ensure our measurements align with the truth, especially in high-stakes industries like healthcare, technology, or manufacturing?

Let’s explore these concepts in detail — with real-world examples and actionable insights that help us move from theoretical knowledge to practical application.

Understanding the Key Terms

1. Precision: Consistency in Results

Precision refers to the repeatability or consistency of measurements. If you perform the same measurement multiple times under the same conditions and get similar results, you're being precise — regardless of whether those results are correct.

Case in Point: In the Indian automotive sector, Tata Motors’ quality control processes focus heavily on precision. Their manufacturing lines use high-precision robotics to ensure every car part meets exact dimensional standards — ensuring consistency, reducing variance, and boosting safety.

Practical Application: We can improve precision by standardizing our measurement procedures and minimizing variation in tools or techniques.

2. Trueness: Closeness to the Real Value

Trueness is about how close your measured value is to the actual, true value. You can be precise but not true — imagine hitting the same wrong spot on a target every time.

Case in Point : In healthcare diagnostics, Dr. Reddy's Laboratories in India has implemented stringent quality checks to ensure lab results align with globally accepted standards — a critical factor in pharmaceutical testing where the 'true' value determines dosage and efficacy.

Practical Application: To improve trueness, we need to identify and eliminate systematic errors. This can include training observers, using reference materials, or comparing results to gold-standard datasets.

3. Accuracy: The Full Picture

Accuracy combines both trueness and precision. It's about getting consistent results that are also correct. Think of it as consistently hitting the bullseye.

Case in Point : The Indian Space Research Organisation (ISRO) exemplifies accuracy in satellite launches. Their Mars Orbiter Mission succeeded not just due to precise engineering but also because of accurate trajectory calculations — a powerful blend of trueness (correct physics) and precision (repeatable engineering).

Practical Application: Strive for processes that reduce both random and systematic errors. Encourage collaboration between departments (engineering, QA, data science) to align measurement practices. Accuracy is the end goal — precision and trueness are how we get there.

Accuracy and Precision:

Accuracy refers to the closeness of a measured value to a standard or known value. Precision refers to the closeness of two or more measurements to each other.

A good analogy for understanding accuracy and precision is to imagine a basketball player shooting baskets. If the player shoots with accuracy, his aim will always take the ball close to or into the basket. If the player shoots with precision, his aim will always take the ball to the same location which may or may not be close to the basket. A good player will be both accurate and precise by shooting the ball the same way each time and each time making it in the basket.

The words precision, trueness and accuracy are important differentiated terms when referring to measurements in the scientific and technical context.  Generally speaking, accuracy refers to how close a measured value is in relation to a known value or standard. However, the International Organization for Standardization (International Standards Organisation - ISO) uses “trueness” for the above definition while keeping the word “accuracy” to refer to the combination of trueness and precision.

On the other hand, precision is related to how close several measurements of the same quantity are to each other. It is rather common to use the terms “bias” and “variability” to refer to the lack of “trueness” and the lack of “precision” respectively.

Precision is sometimes stratified into:

o   Repeatability — the variation arising when all efforts are made to keep conditions constant by using the same instrument and operator, and repeating during a short time period; and

o   Reproducibility — the variation arising using the same measurement process among different instruments and operators, and over longer time periods.

With regard to Accuracy we can distinguish:

o   The difference between the mean of the measurements and the reference value, the bias. Establishing and correcting for bias is necessary for calibration.

o   The combined effect of that and precision.

Common Misconceptions (And How to Avoid Them)

“If it’s precise, it must be accurate.”

→ Not necessarily. A broken thermometer can give consistent, but wrong, readings.

“One good result means the system is fine.”

→ One accurate result doesn't guarantee trueness or precision across trials.

“Trueness and accuracy are the same.”

→ Accuracy is broader and includes both trueness and precision.

Four Possible States of a Measurement System

It is important to realise that when since accuracy and precision have different meanings in the context of measurement systems, there are 4 possible states that a measurement system can have in this regard. The measurement system under consideration maybe:

1)      Both accurate and precise

2)      Accurate but not precise

3)      Precise but not accurate

4)      Neither accurate nor precise

When accuracy and precision are present in the system together, it gives measurements that are close to the standard value and to each other. This is the desired state of affairs that every measurement system eventually works toward.

The ISO standard 5725, under the title “Accuracy (trueness and precision) of measurement methods and results”, uses the combination of two terms, “trueness” and “precision”, to describe the accuracy of a measurement method. According to ISO 5725, “Trueness” refers to the closeness of agreement between the arithmetic mean of a large number of test results and the true or accepted reference value. “Precision” refers to the closeness of agreement between different test results.

On the other hand, the Bureau International des Poids et Mesures (BIPM) defines accuracy as the closeness of agreement between a measured quantity value and a true quantity value of a measurand (quantity intended to be measured). In this case, trueness is defined as the closeness of agreement between the average of an infinite number of replicate measured quantity values and a reference quantity value.

Tightly related to accuracy, trueness and precision, is the measurement error, also referred as an observational error. This error that can be quantified by different methods is defined as the difference between the “true” value and the measured value. The systematic part of the observational error is generally related to the trueness of the measurement while its random part is linked to precision

Accuracy and precision in psychometrics and psychophysics

In psychometrics and psychophysics, the term accuracy is interchangeably used with validity and 'constant error', whereas 'precision' is a synonym for reliability and 'variable error' respectively. Validity of a measurement instrument or psychological test is established through experiment or correlation with behavior. Reliability is established with a variety of statistical technique (classically Cronbach's alpha).

Applications Across Industries

1. Manufacturing: Precision ensures standardized production. Trueness ensures components fit and function as designed. Mahindra & Mahindra’s lean manufacturing approach exemplifies this dual focus.

2. Healthcare: Accurate lab diagnostics can mean the difference between early detection and a missed diagnosis. Metropolis Healthcare in India has adopted automated reporting systems to minimize human error and boost both precision and trueness.

3. Data Science: In predictive analytics, precision refers to consistent model outputs, while trueness relates to how close predictions are to actual outcomes. TCS uses advanced data validation techniques in their AI platforms to enhance both.

Getting It Right Together

In fast-moving environments where accuracy defines credibility, it's vital that we not only understand the difference between precision, trueness, and accuracy, but also know how to apply them in real-world contexts. By refining how we measure, calibrate, and collaborate, we can move closer to insights — and outcomes — that truly matter.

Whether you're building cars, designing satellites, or developing AI systems, these distinctions aren't just semantics. They're the difference between success and setback.

Content Curated By: Dr Shoury Kuttappa