The Different Types of Measurement Error Explained

September 5, 2019 - Emily Newton

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When most of us read scientific papers or the press releases about them, we assume that the measurements the scientists make during their tests are accurate, so we trust them. But scientific researchers are humans too, and they can make mistakes when measuring things. There are three broad types of measurement error in science.

We’ll go over them here so that you can be aware of them as well as avoid making the same mistakes if you carry out your own experiments. First, keep in mind that an error is a discrepancy between the actual values and the observed ones. If a researcher accepts an error as fact and publicizes those results, it could mislead or misinform the audience.

Becoming Familiar With Random Error

Random error happens due to unknown, unpredictable and uncontrollable changes in an experiment. The impact a random error has on a measurement varies each time. For example, an environmental factor such as wind speed could cause a random error. Consider a scientist trying to take an outdoor measurement on a day characterized by lots of sporadic but intense wind gusts. If they attempt to read an instrument as a sudden gust happens, that environmental factor could result in random error.

Engaging in a process called replication by taking several measurements to determine the average is one dependable way to remove the influence of random errors. Each measurement will have fluctuations, but the collective results should help you recognize the correct measurement versus the outliers that are the mistakes. Alternatively, consider refining your measurement technique to make the uncontrollable factors less problematic.

Taking such an approach requires patience and a willingness to engage in trial and error, but it could pay off if accurate results are a priority.

What About Systematic Error?

Researchers also must watch out for and minimize systematic error while doing their experiments. Unlike random error, systematic error is predictable as long as a person takes the reading in the same way in time or uses their instruments without making changes. In that case, the error affects the results by the same proportion with each attempt. Since systematic errors are usually consistently positive or negative, some people categorize them as bias.

Several kinds of systematic error exist. The first is instrumental error. It could happen with an uncalibrated instrument or if the measurement tool is broken and the user doesn’t realize it. Procedural errors can also happen, such as when multiple researchers round numbers and don’t agree on the procedure at the start. If one person rounds up and others round down, discrepancies creep into the findings. Then, there are environmental errors, which are different from the fluctuating conditions described in the previous section. They’re associated with the constant environmental conditions that a researcher could control wherever they use a measurement tool.

For example, if a researcher takes an indoor measurement in a room that’s too humid for the instrument to function properly, environmental errors could taint their results. Theoretical errors can pose problems as well. When a person relies too heavily on an overly simplistic theory associated with their measuring equipment, they could fall into a pattern of paying too much to that aspect while overlooking others. Finally, there are observational errors. They happen when people misread their measuring instruments.

Human Error Causes Blunders

Whether people do experiments as total novices or experienced professionals, they should all follow the steps of the scientific method. Making a hypothesis and testing it are a couple of the steps that you’ve probably heard of before. Passion and enthusiasm are two things that can make your science explorations especially rewarding, but you should never let your excitement get in the way of accuracy.

When people rush through taking measurements or never put in the time to learn how to take them correctly, blunders may adversely affect the validity of their results. These kinds of issues are also called gross errors. You can think of them as any inaccuracies caused by human error. Some observational errors are blunders as well. The two types of blunders typically discussed in the scientific community are estimation errors and transcriptional errors. An estimation error can happen if it’s not possible to get an exact reading with a measurement tool and the people reading the results cannot agree.

One might also occur if you make an estimation and don’t ask a friend or colleague to confirm what you saw. Then, transcription errors relate to the mistakes that happen if you write a figure down incorrectly or put it in the wrong field on a spreadsheet, for example. If you hurriedly scrawl down your measurement in sloppy handwriting that other people can’t read, a transcription error could quickly spread throughout an organization as colleagues make assumptions.

Being careful is one of the most effective approaches for reducing gross errors. It’s also smart to have another person take measurements after you do and compare the results.

What Impacts Do the Types of Measurement Error Have?

Now that you’ve gotten this breakdown of the measurement mistakes scientists sometimes make, you might wonder about the potential ramifications. The severity of the errors is one factor that makes a difference, as does the number of people or entities affected. When

Facebook admitted an assortment of mistakes associated with marketing and engagement metrics in 2016 and 2017, news broke that one of the errors made marketers get billed incorrectly for advertising on the social media platform. Some of Facebook’s customers may decide that those mishaps are unacceptable and look for other promotional methods.

More recently, a scientific team issued corrections to a published paper. They realized that the margin of error associated with research on how climate change affected warming ocean temperatures was much greater than first calculated. The revelation meant they could not put as much confidence in their conclusions. In that case, the researchers admitted their mistakes. They also committed to taking all concerns raised about their findings and re-examining the methods they used in the study. Those responses convey that they’re serious about fixing inaccuracies.

Continual Improvement Is an Excellent Goal

No one is perfect, and you’ll inevitably make some mistakes in your research. That said, knowing the types of measurement error and how to avoid them means you’ll be more likely to achieve accurate results. When you find problems with measurements, decide you’ll work even harder to improve rather than getting overly frustrated. That way, you’ll learn from your mistakes, avoid making them again and come closer to perfecting your process as time goes on.

Revolutionized is reader-supported. When you buy through links on our site, we may earn an affiliate commision. Learn more here.


Emily Newton

Emily Newton is a technology and industrial journalist and the Editor in Chief of Revolutionized. She enjoys reading and writing about how technology is changing the world around us.

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