How to Calculate Percentage Error?

How to Calculate Percentage Error?

How to Calculate Percentage Error? The accuracy of a measurement or calculation is gauged by its percentage error. It is the discrepancy, represented as a percentage of the real value, between the measured or computed value and the actual value. We will talk about the many elements of % error calculation in this blog.

Understanding Percentage Error

Percentage error is an important concept in science and engineering, where precise measurements are critical. It is used to quantify the accuracy of a measurement or calculation. Percentage error can be positive or negative, depending on whether the measured or calculated value is higher or lower than the actual value.

Formula for Percentage Error

The percentage error formula is:

(Measured Value – Actual Value) / Actual Value x 100% is the formula for percentage error.


Measured Value = The value obtained from the measurement or calculation.

Actual Value = The true or expected value of the quantity being measured or calculated.

Calculating Percentage Error

Measuring the accuracy of a calculation or experiment requires considering the possibility of errors. Even the most precise instruments and calculations always have a margin of error. Therefore, it’s crucial to learn how to calculate percentage error accurately. This blog will cover the basics of percentage error, including how to calculate it and some common types of errors.

What is Percentage Error?

Percentage error, also known as percent error, is a way to measure the difference between the actual value and the estimated or calculated value of a measurement. It’s a useful tool to assess the accuracy of an experiment, model, or calculation. Percentage error is expressed as a percentage of the actual value.

Types of Errors

Before we dive into calculating percentage error, let’s look at the two primary types of errors that can occur: systematic and random errors.

Systematic Errors

Systematic errors are predictable and repeatable errors that occur consistently in the same direction. These errors arise due to faulty equipment, incorrect measurement techniques, or incorrect calibration. Systematic errors can be corrected by adjusting or replacing the faulty equipment or calibration.

Random Errors

Random errors are unpredictable and inconsistent errors that occur randomly in both directions. They arise from environmental factors, human error, or natural variations in the system. Random errors can be minimized by taking multiple measurements and calculating the average.

Let’s start the calculations now. By comparing the absolute difference between the real value and the estimated value to the actual value, the percentage error is determined. The percentage error formula is as follows:

Percentage Error = [(|Estimated Value – Actual Value|) / Actual Value] x 100%

Here’s an example to help illustrate this formula:

Tips for Reducing Percentage Error

Here are some tips to reduce the percentage error in your measurements:

Calibrate your equipment regularly to ensure accuracy.

Use appropriate measurement techniques to minimize systematic errors.

Take multiple measurements to reduce the impact of random errors.

Be aware of environmental factors that may affect your measurements.

Always use the correct units when making measurements.

Factors Affecting Percentage Error

Measurement is an essential part of science and engineering, and it is crucial to ensure that the measurements are accurate and precise. However, no measurement is perfect, and there is always some degree of error associated with it. Percentage error is one way to quantify the error in a measurement. It is the difference between the measured value and the true value, expressed as a percentage of the true value. There are several factors that can affect the percentage error in a measurement. In this blog, we will explore some of the most important ones.

Instrument Error

Every instrument has some degree of error associated with it, which is typically specified by the manufacturer. This error can arise from various sources, such as the design, manufacturing process, or calibration. The instrument error can be classified into two types: systematic and random errors. Systematic errors are due to a flaw in the instrument design or calibration and tend to affect all measurements in the same way. On the other hand, random errors are due to the inherent variability in the measurement process and tend to affect measurements in a random manner. Both types of errors contribute to the overall percentage error in a measurement.

Environmental Factors

Environmental factors such as temperature, humidity, air pressure, among others, can all have an impact on the measurement. For instance, taking a length measurement in a hot and humid environment may yield a different result from taking the same measurement in a cold and dry environment. Likewise, taking a pressure measurement at a high altitude may yield a different result from taking the same measurement at sea level. To reduce the impact of environmental factors on the measurement, it is essential to control the environment as much as possible and conduct measurements under standard conditions.

Human Error

Human error is another factor that can contribute to the percentage error in a measurement. This can include errors in reading the instrument, recording the data, or performing the measurement. For example, a person may misread the scale on a thermometer or forget to record a data point. To minimize human error, it is essential to train the operator on the proper use of the instrument and measurement technique, and to implement quality control procedures to verify the accuracy of the measurements.

Sample Variation

The variation in the sample being measured can also affect the percentage error. This can be due to variations in the material properties, such as density or viscosity, or due to variations in the sample preparation or handling. For example, the percentage error in a viscosity measurement may be higher if the sample is not properly mixed before the measurement. To minimize sample variation, it is essential to prepare the sample properly and handle it consistently throughout the measurement process.

Measurement Technique

The measurement technique can also impact the percentage error. Factors such as the measurement method, the instrument, and the data analysis technique contribute to this. For instance, using a mercury thermometer instead of a digital thermometer may result in a different percentage error in temperature measurement. Likewise, using a volumetric method instead of a mass flow method may result in a different percentage error in flow rate measurement. Using appropriate measurement methods and instruments and correctly analyzing the data are essential to minimize the impact of the measurement technique on the percentage error.

How to Calculate Percentage Error?

Minimizing Percentage Error

Data analysis is an essential part of decision-making processes in various industries, including finance, healthcare, and marketing. However, data analysis is not always accurate due to various factors such as data collection errors, sampling bias, and inappropriate analysis methods. One way to measure the accuracy of data analysis is by calculating the percentage error, which represents the difference between the observed and expected values. In this blog, we will discuss some strategies to minimize percentage error in data analysis.

Understanding Percentage Error

Percentage error is a measure of the accuracy of data analysis that represents the proportion of the expected value that represents the difference between actual and expected values. For determining percentage errors, use the formula:

Percentage Error = [(Observed Value – Expected Value)/Expected Value] x 100%

For instance, if the expected value is 100 and the observed value is 95, the percentage error would be:

Percentage Error = [(95-100)/100] x 100% = -5%

This means that the observed value is 5% lower than the expected value.

Factors Affecting Percentage Error

Before discussing strategies to minimize percentage error, it is important to understand the factors that affect percentage error. These include:

Errors in Data Collection: Data that is erroneous causes data collection mistakes, incomplete, or inconsistent. This can happen due to various reasons such as human error, measurement errors, or technical errors.

Sampling Bias: When the sample utilized for analysis is unrepresentative of the population under study, sampling bias arises. This may occur if the sample is too small, biased, or lacking in randomness.

Inappropriate Analysis Methods: Inappropriate analysis methods can lead to inaccurate results. This can happen when the wrong statistical method is used, or the data is not analyzed correctly.

Strategies to Minimize Percentage Error

Improve Data Quality: Improving data quality is essential to minimize percentage error. This can be done by using accurate data collection methods, validating the data, and cleaning the data to remove any errors or inconsistencies.

Increase Sample Size: Increasing the sample size can help reduce sampling bias and improve the accuracy of data analysis. A larger sample size reduces the impact of random variations and increases the representation of the population being studied.

Use Random Sampling Methods: Using random sampling techniques can lessen sampling bias and improve sample representativeness. Every member of the population has an equal chance of being chosen for the sample thanks to random sampling.

Use Appropriate Analysis Methods: Using appropriate analysis methods is crucial to minimize percentage error. This involves selecting the right statistical method for the data and ensuring that the analysis is conducted correctly.

Validate Results: Validating the results of data analysis is important to ensure accuracy. This can be done by comparing the results with previous studies or industry standards, conducting sensitivity analysis, or cross-validation.

Use Multiple Data Sources: Using multiple data sources can help reduce the impact of data collection errors and increase the reliability of data analysis. This involves collecting data from different sources and comparing the results to identify any inconsistencies or errors.

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