human errors in experiments examples

What Kind of Human Errors Can Occur During Experiments?

25 jun 2018.

Human errors can be described as bumbling mistakes made during an experiment that can invalidate your data and conclusions. Scientists recognize that experimental findings may be imprecise due to variables difficult to control, such as changes in room temperature, slight miscalibrations in lab instruments, or a flawed research design. However, scientists and college professors have little tolerance for human errors occurring due to carelessness or sloppy technique. If you know you really messed up, redo the experiment.

Explore this article

• Failure to Follow Directions
• Mishaps in Measuring
• Contamination of Materials
• Miscalculations of Data

1 Failure to Follow Directions

Before leaping into a laboratory activity, carefully read the instructions in the lab manual thinking about the purpose of the experiment and possible results. If you don’t understand a step, consult with your lab partner or instructor before proceeding. Perform each step of the experiment in the correct order to the best of your ability. Don’t attempt shortcuts in the procedure to save time. Conducting an experiment is similar to following a recipe in the kitchen but far more exacting. Even slight deviations can change your results in dramatic ways.

2 Mishaps in Measuring

Spilling chemicals when measuring, using the wrong amount of solution, or forgetting to add a chemical compound are mistakes commonly made by students in introductory science labs. Measurement errors can result in flawed data, faulty conclusions and a low grade on your lab report. Worse still, you may cause a dangerous chemical reaction. Ask your lab instructor for guidance if you know your measurements are way off from the instructions; sometimes the experiment or your calculations can be adjusted to avoid starting over. It is better to be safe than to risk injury to yourself and others.

3 Contamination of Materials

Failing to maintain sterile conditions can cause contamination and produce unwanted results in your experiment. For example, coughing or breathing into the petri dish when inoculating nutrient agar with a certain type of bacteria can introduce other bacterial strains that may also grow on your culture. Mold spores and dust can harm your experiment if you forget to wipe down your work area with alcohol. Touching the tip of a pipette before using it to transfer liquids during your experiment can also affect results.

4 Miscalculations of Data

Data errors such as applying the wrong mathematical formula, miscalculating answers, or placing the decimal in the wrong place can adversely impact an experiment by skewing your results. Failure to carefully observe and record raw data can be problematic when later attempting to analyze your data. Keeping a detailed, written log of your lab activities can help you learn from your mistakes. Dartmouth University recommends that students keep a permanent lab notebook for documenting their techniques, procedures, calculations and findings for accuracy and quality control.

• 1 Chemistry Purdue University: Sources of Error in Pipetting
• 2 Physics Kansas State University: Part G: Methods and Materials

About the Author

Dr. Mary Dowd is a dean of students whose job includes student conduct, leading the behavioral consultation team, crisis response, retention and the working with the veterans resource center. She enjoys helping parents and students solve problems through advising, teaching and writing online articles that appear on many sites. Dr. Dowd also contributes to scholarly books and journal articles.

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Understanding Experimental Errors: Types, Causes, and Solutions

Types of experimental errors.

In scientific experiments, errors can occur that affect the accuracy and reliability of the results. These errors are often classified into three main categories: systematic errors, random errors, and human errors. Here are some common types of experimental errors:

1. Systematic Errors

Systematic errors are consistent and predictable errors that occur throughout an experiment. They can arise from flaws in equipment, calibration issues, or flawed experimental design. Some examples of systematic errors include:

– Instrumental Errors: These errors occur due to inaccuracies or limitations of the measuring instruments used in the experiment. For example, a thermometer may consistently read temperatures slightly higher or lower than the actual value.

– Environmental Errors: Changes in environmental conditions, such as temperature or humidity, can introduce systematic errors. For instance, if an experiment requires precise temperature control, fluctuations in the room temperature can impact the results.

– Procedural Errors: Errors in following the experimental procedure can lead to systematic errors. This can include improper mixing of reagents, incorrect timing, or using the wrong formula or equation.

2. Random Errors

Random errors are unpredictable variations that occur during an experiment. They can arise from factors such as inherent limitations of measurement tools, natural fluctuations in data, or human variability. Random errors can occur independently in each measurement and can cause data points to scatter around the true value. Some examples of random errors include:

– Instrument Noise: Instruments may introduce random noise into the measurements, resulting in small variations in the recorded data.

– Biological Variability: In experiments involving living organisms, natural biological variability can contribute to random errors. For example, in studies involving human subjects, individual differences in response to a treatment can introduce variability.

– Reading Errors: When taking measurements, human observers can introduce random errors due to imprecise readings or misinterpretation of data.

3. Human Errors

Human errors are mistakes or inaccuracies that occur due to human factors, such as lack of attention, improper technique, or inadequate training. These errors can significantly impact the experimental results. Some examples of human errors include:

– Data Entry Errors: Mistakes made when recording data or entering data into a computer can introduce errors. These errors can occur due to typographical mistakes, transposition errors, or misinterpretation of results.

– Calculation Errors: Errors in mathematical calculations can occur during data analysis or when performing calculations required for the experiment. These errors can result from mathematical mistakes, incorrect formulas, or rounding errors.

– Experimental Bias: Personal biases or preconceived notions held by the experimenter can introduce bias into the experiment, leading to inaccurate results.

It is crucial for scientists to be aware of these types of errors and take measures to minimize their impact on experimental outcomes. This includes careful experimental design, proper calibration of instruments, multiple repetitions of measurements, and thorough documentation of procedures and observations.

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#Compliance #Quality Management

Is human error a root cause? A guide to human error in Root Cause Analysis

Root Cause Analysis (RCA) is a systematic process used to identify the underlying cause or causes that lead to a problem or incident, with the goal of preventing its recurrence in the future. It is a systematic approach employed in various industries, including the life sciences. The purpose is to understand the fundamental reason(s) behind an issue, rather than just addressing its symptoms. RCA aims to identify what, how, and why an event occurred, allowing organizations to implement effective corrective and preventive actions .

In the life sciences industry, RCA is used for reporting and investigating product defects, process failures, compliance failures, adverse events, patient safety issues, clinical trial failures, etc.. The responsible stakeholders involved in the investigation of the concerned defect or failure often assemble in the form of a cross-functional team and assign probable root causes for the failure or defect through a brainstorming session. For clarity, root cause analysis is often performed with the help of tools such as the Ishikawa Diagram (Fishbone diagram), Five Whys Technique, Failure Mode Effect Analysis , Fault Tree Analysis, and Risk Ranking. 

Is human error really a root cause?

Citing human error as a root cause is the easiest way to ignore underlying issues and unfortunately, it happens more often than it should. For example, while using the Ishikawa Diagram for the RCA,  the team classifies all the probable root causes into the 5M categories as a starting point. These categories are often labeled as Manpower, Material, Method, Machine, Measurement, and Miscellaneous causes. Out of these five categories, the Manpower related root causes are the most debated ones as regrettably, this category is exploited as a scapegoat under the label of ‘human error’.  The same can be true for other RCA techniques when the team lands on a probable root cause as ‘human error’ during the investigation process.  Landing on a human error as the root cause too often is undesirable as it starts a never-ending passing-the-buck game that spoils your company’s quality culture, and at worst, the real issues remain unaddressed resulting in multiple recurrences. Therefore, in this article we will discuss how to handle a recurring failure that is often attributed to human error, knowing that human error can not really be a root cause. Do you want to dive deeper into the Root Cause Analysis topic? Watch our training below! 

What is behind human error?

The process of RCA involves running your train of thought until you have no further questions to ask. Your train of thought should only stop when you see a direct correlation with the problem. Often when you label an issue as ‘human error’ you may still have some difficult questions unanswered. If you think deeper these issues will point toward your systemic flaws.  Let’s take a simple example of parallax error that occurs when two different scientists try to measure the volume of a liquid using a measuring cylinder. The error could potentially occur because one scientist measured the volume by observing the lower meniscus while the other did it by measuring the upper meniscus. It could be tempting to label the error as ‘human error’ but in reality, there could be systemic issues such as the inexistence of a standard operating procedure that describes which meniscus should be measured for the specific liquid. In FDA-regulated manufacturing, you are not only expected to point towards causes but also eliminate them through proper Corrective and Preventive Actions . If you label the issue as ‘human error’ and do nothing about it, then you deny the possibility of improving your quality management system. Having an issue that is frequently labeled as ‘human error’ indicates that your quality management is person-dependent and not system-dependent. Such systemic flaws may be the hidden culprits in many FDA-regulated scenarios.   It’s time we unveil these masked issues using cognitive psychology models and build robust quality management systems to handle the real issues behind human error.

A model for analyzing Human Error

It is essential to objectively investigate the causes labeled as human error using human factors engineering and cognitive psychology models such as the Skills, Rules, Knowledge (SRK) framework. It is used to understand how people perform tasks and make conclusive decisions. Let’s understand each term in the SRK framework and the cognitive processes involved in decision-making:

Skills (S):

Definition: Skills refer to actions that are performed automatically and without conscious thought. These are tasks that have been practiced to the point where they become almost instinctive. Example: Skills could include basic laboratory techniques, pipetting accurately, or using specific software for data analysis. These tasks have been practiced to the point where they can be done efficiently without much cognitive effort.

Definition: Rules are decision-making processes based on explicit guidelines or protocols. When a situation is recognized, specific rules are applied to determine the appropriate action. Example: Life sciences professionals often follow predefined standard operating procedures, work instructions, and regulatory guidelines when conducting experiments or analyzing data. These rules help in making accurate decisions in various situations. 

Knowledge (K):

Definition: Knowledge represents understanding and problem-solving ability. It involves the application of general principles and strategies to solve new or unfamiliar problems. Example: Life sciences employees rely on their knowledge of biological processes, experimental design, and statistical methods to solve complex research problems. This knowledge is beyond following established protocols (rules) or performing routine tasks (skills) but also about understanding the underlying principles and adapting to unique situations.

The SRK model helps us understand the cognitive processes involved in the decision-making process. Once we know the cognitive processes that lead to human errors, we can make improvements in the decision-making process using a Generic Error Modeling System (GEMS) as follows:

• Definition: GEMS is a framework used to identify and analyze human errors in complex systems. It categorizes errors based on cognitive processes, helping professionals understand how and why errors occur.
• Application: GEMS can be used to analyze errors made during experiments, data analysis, or interpretation of results. By understanding the cognitive processes behind errors (such as misapplying rules or misunderstanding underlying knowledge), organizations can implement strategies to minimize the likelihood of these errors occurring in the future.

In this manner, the integration of the SRK framework and the Generic Error Modeling System (GEMS) further allows for a systematic analysis of errors, leading to improvements in processes and reducing the likelihood of future mistakes.

What can we do to avoid it ?

Now that we have understood the SRK and GEMS framework, let’s find out what could be an appropriate action plan under different circumstances. According to our understanding of the cognitive root cause, we can implement different problem-solving strategies. Below are a few examples:

• Skill-based errors often occur due to momentary inattention or temporary memory loss, leading to slips or lapses. By addressing distractions and improving focus, we can reduce these errors.
• Knowledge-based errors arise when individuals are overwhelmed by multitasking or lacking focus. To mitigate these errors, we need to ask: Why is multitasking happening? Are the departments under-resourced? Is there a lack of awareness of an individual’s workload?
• Rule-based errors often result from misapplication of rules or procedures. To address these, it’s crucial to ensure adequate training and detailed, clear standard operating procedures (SOPs) . If training falls short, we must examine why it’s failing to prepare individuals for their tasks.
• Systemic issues in documentation can also contribute to errors. Streamlining documentation processes, ensuring up-to-date SOPs, and making them easily accessible can significantly reduce errors.

The idea is to build a quality culture by creating an effective Corrective and Preventive Action Plan rather than avoiding it and digging deeper into the problem and identifying a superficial root cause with no Corrective and Preventive Actions. Most importantly, labeling the root cause as ‘human error’ doesn’t put an end to the story, it is where the story begins. Our responsibility is to create a robust system that doesn’t let ‘human error’ occur in the first place.   We’ve all had those moments when human error threw a wrench in the works. Share your story and the lessons you’ve learned!

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Methodology

• Random vs. Systematic Error | Definition & Examples

Random vs. Systematic Error | Definition & Examples

Published on May 7, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In scientific research, measurement error is the difference between an observed value and the true value of something. It’s also called observation error or experimental error.

There are two main types of measurement error:

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

• Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently registers weights as higher than they actually are).

By recognizing the sources of error, you can reduce their impacts and record accurate and precise measurements. Gone unnoticed, these errors can lead to research biases like omitted variable bias or information bias .

Table of contents

Are random or systematic errors worse, random error, reducing random error, systematic error, reducing systematic error, other interesting articles, frequently asked questions about random and systematic error.

In research, systematic errors are generally a bigger problem than random errors.

Random error isn’t necessarily a mistake, but rather a natural part of measurement. There is always some variability in measurements, even when you measure the same thing repeatedly, because of fluctuations in the environment, the instrument, or your own interpretations.

But variability can be a problem when it affects your ability to draw valid conclusions about relationships between variables . This is more likely to occur as a result of systematic error.

Precision vs accuracy

Random error mainly affects precision , which is how reproducible the same measurement is under equivalent circumstances. In contrast, systematic error affects the accuracy of a measurement, or how close the observed value is to the true value.

Taking measurements is similar to hitting a central target on a dartboard. For accurate measurements, you aim to get your dart (your observations) as close to the target (the true values) as you possibly can. For precise measurements, you aim to get repeated observations as close to each other as possible.

Random error introduces variability between different measurements of the same thing, while systematic error skews your measurement away from the true value in a specific direction.

When you only have random error, if you measure the same thing multiple times, your measurements will tend to cluster or vary around the true value. Some values will be higher than the true score, while others will be lower. When you average out these measurements, you’ll get very close to the true score.

For this reason, random error isn’t considered a big problem when you’re collecting data from a large sample—the errors in different directions will cancel each other out when you calculate descriptive statistics . But it could affect the precision of your dataset when you have a small sample.

Systematic errors are much more problematic than random errors because they can skew your data to lead you to false conclusions. If you have systematic error, your measurements will be biased away from the true values. Ultimately, you might make a false positive or a false negative conclusion (a Type I or II error ) about the relationship between the variables you’re studying.

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Random error affects your measurements in unpredictable ways: your measurements are equally likely to be higher or lower than the true values.

In the graph below, the black line represents a perfect match between the true scores and observed scores of a scale. In an ideal world, all of your data would fall on exactly that line. The green dots represent the actual observed scores for each measurement with random error added.

Random error is referred to as “noise”, because it blurs the true value (or the “signal”) of what’s being measured. Keeping random error low helps you collect precise data.

Sources of random errors

Some common sources of random error include:

• natural variations in real world or experimental contexts.
• imprecise or unreliable measurement instruments.
• individual differences between participants or units.
• poorly controlled experimental procedures.

Random error is almost always present in research, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error using the following methods.

Take repeated measurements

A simple way to increase precision is by taking repeated measurements and using their average. For example, you might measure the wrist circumference of a participant three times and get slightly different lengths each time. Taking the mean of the three measurements, instead of using just one, brings you much closer to the true value.

Increase your sample size

Large samples have less random error than small samples. That’s because the errors in different directions cancel each other out more efficiently when you have more data points. Collecting data from a large sample increases precision and statistical power .

Control variables

In controlled experiments , you should carefully control any extraneous variables that could impact your measurements. These should be controlled for all participants so that you remove key sources of random error across the board.

Systematic error means that your measurements of the same thing will vary in predictable ways: every measurement will differ from the true measurement in the same direction, and even by the same amount in some cases.

Systematic error is also referred to as bias because your data is skewed in standardized ways that hide the true values. This may lead to inaccurate conclusions.

Types of systematic errors

Offset errors and scale factor errors are two quantifiable types of systematic error.

An offset error occurs when a scale isn’t calibrated to a correct zero point. It’s also called an additive error or a zero-setting error.

A scale factor error is when measurements consistently differ from the true value proportionally (e.g., by 10%). It’s also referred to as a correlational systematic error or a multiplier error.

You can plot offset errors and scale factor errors in graphs to identify their differences. In the graphs below, the black line shows when your observed value is the exact true value, and there is no random error.

The blue line is an offset error: it shifts all of your observed values upwards or downwards by a fixed amount (here, it’s one additional unit).

The purple line is a scale factor error: all of your observed values are multiplied by a factor—all values are shifted in the same direction by the same proportion, but by different absolute amounts.

Sources of systematic errors

The sources of systematic error can range from your research materials to your data collection procedures and to your analysis techniques. This isn’t an exhaustive list of systematic error sources, because they can come from all aspects of research.

Response bias occurs when your research materials (e.g., questionnaires ) prompt participants to answer or act in inauthentic ways through leading questions . For example, social desirability bias can lead participants try to conform to societal norms, even if that’s not how they truly feel.

Your question states: “Experts believe that only systematic actions can reduce the effects of climate change. Do you agree that individual actions are pointless?”

Experimenter drift occurs when observers become fatigued, bored, or less motivated after long periods of data collection or coding, and they slowly depart from using standardized procedures in identifiable ways.

Initially, you code all subtle and obvious behaviors that fit your criteria as cooperative. But after spending days on this task, you only code extremely obviously helpful actions as cooperative.

Sampling bias occurs when some members of a population are more likely to be included in your study than others. It reduces the generalizability of your findings, because your sample isn’t representative of the whole population.

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You can reduce systematic errors by implementing these methods in your study.

Triangulation

Triangulation means using multiple techniques to record observations so that you’re not relying on only one instrument or method.

For example, if you’re measuring stress levels, you can use survey responses, physiological recordings, and reaction times as indicators. You can check whether all three of these measurements converge or overlap to make sure that your results don’t depend on the exact instrument used.

Regular calibration

Calibrating an instrument means comparing what the instrument records with the true value of a known, standard quantity. Regularly calibrating your instrument with an accurate reference helps reduce the likelihood of systematic errors affecting your study.

You can also calibrate observers or researchers in terms of how they code or record data. Use standard protocols and routine checks to avoid experimenter drift.

Randomization

Probability sampling methods help ensure that your sample doesn’t systematically differ from the population.

In addition, if you’re doing an experiment, use random assignment to place participants into different treatment conditions. This helps counter bias by balancing participant characteristics across groups.

Wherever possible, you should hide the condition assignment from participants and researchers through masking (blinding) .

Participants’ behaviors or responses can be influenced by experimenter expectancies and demand characteristics in the environment, so controlling these will help you reduce systematic bias.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Random and systematic error are two types of measurement error.

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

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Types of Error — Overview & Comparison - Expii

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Systematic vs Random Error – Differences and Examples

Systematic and random error are an inevitable part of measurement. Error is not an accident or mistake. It naturally results from the instruments we use, the way we use them, and factors outside our control. Take a look at what systematic and random error are, get examples, and learn how to minimize their effects on measurements.

• Systematic error has the same value or proportion for every measurement, while random error fluctuates unpredictably.
• Systematic error primarily reduces measurement accuracy, while random error reduces measurement precision.
• It’s possible to reduce systematic error, but random error cannot be eliminated.

Systematic vs Random Error

Systematic error is consistent, reproducible error that is not determined by chance. Systematic error introduces inaccuracy into measurements, even though they may be precise. Averaging repeated measurements does not reduce systematic error, but calibrating instruments helps. Systematic error always occurs and has the same value when repeating measurements the same way.

As its name suggests, random error is inconsistent error caused by chance differences that occur when taking repeated measurements. Random error reduces measurement precision, but measurements cluster around the true value. Averaging measurements containing only random error gives an accurate, imprecise value. Random errors cannot be controlled and are not the same from one measurement to the next.

Systematic Error Examples and Causes

Systematic error is consistent or proportional to the measurement, so it primarily affects accuracy. Causes of systematic error include poor instrument calibration, environmental influence, and imperfect measurement technique.

Here are examples of systematic error:

• Reading a meniscus above or below eye level always gives an inaccurate reading. The reading is consistently high or low, depending on the viewing angle.
• A scale gives a mass measurement that is always “off” by a set amount. This is called an offset error . Taring or zeroing a scale counteracts this error.
• Metal rulers consistently give different measurements when they are cold compared to when they are hot due to thermal expansion. Reducing this error means using a ruler at the temperature at which it was calibrated.
• An improperly calibrated thermometer gives accurate readings within a normal temperature range. But, readings become less accurate at higher or lower temperatures.
• An old, stretched cloth measuring tape gives consistent, but different measurements than a new tape. Proportional errors of this type are called scale factor errors .
• Drift occurs when successive measurements become consistently higher or lower as time progresses. Electronic equipment is susceptible to drift. Devices that warm up tend to experience positive drift. In some cases, the solution is to wait until an instrument warms up before using it. In other cases, it’s important to calibrate equipment to account for drift.

How to Reduce Systematic Error

Once you recognize systematic error, it’s possible to reduce it. This involves calibrating equipment, warming up instruments because taking readings, comparing values against standards, and using experimental controls. You’ll get less systematic error if you have experience with a measuring instrument and know its limitations. Randomizing sampling methods also helps, particularly when drift is a concern.

Random Error Examples and Causes

Random error causes measurements to cluster around the true value, so it primarily affects precision. Causes of random error include instrument limitations, minor variations in measuring techniques, and environmental factors.

Here are examples of random error:

• Posture changes affect height measurements.
• Reaction speed affects timing measurements.
• Slight variations in viewing angle affect volume measurements.
• Wind velocity and direction measurements naturally vary according to the time at which they are taken. Averaging several measurements gives a more accurate value.
• Readings that fall between the marks on a device must be estimated. To some extent, its possible to minimize this error by choosing an appropriate instrument. For example, volume measurements are more precise using a graduated cylinder instead of a beaker.
• Mass measurements on an analytical balance vary with air currents and tiny mass changes in the sample.
• Weight measurements on a scale vary because it’s impossible to stand on the scale exactly the same way each time. Averaging multiple measurements minimizes the error.

How to Reduce Random Error

It’s not possible to eliminate random error, but there are ways to minimize its effect. Repeat measurements or increase sample size. Be sure to average data to offset the influence of chance.

Which Types of Error Is Worse?

Systematic errors are a bigger problem than random errors. This is because random errors affect precision, but it’s possible to average multiple measurements to get an accurate value. In contrast, systematic errors affect precision. Unless the error is recognized, measurements with systematic errors may be far from true values.

• Bland, J. Martin, and Douglas G. Altman (1996). “Statistics Notes: Measurement Error.”  BMJ  313.7059: 744.
• Cochran, W. G. (1968). “Errors of Measurement in Statistics”.  Technometrics . Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality. 10: 637–666. doi: 10.2307/1267450
• Dodge, Y. (2003).  The Oxford Dictionary of Statistical Terms . OUP. ISBN 0-19-920613-9.
• Taylor, J. R. (1999).  An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements . University Science Books. ISBN 0-935702-75-X.

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Random Error vs. Systematic Error

Two Types of Experimental Error

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
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No matter how careful you are, there is always some error in a measurement. Error is not a "mistake"—it's part of the measuring process. In science, measurement error is called experimental error or observational error.

There are two broad classes of observational errors: random error and systematic error. Random error varies unpredictably from one measurement to another, while systematic error has the same value or proportion for every measurement. Random errors are unavoidable but cluster around the true value. Systematic error can often be avoided by calibrating equipment, but if left uncorrected, it can lead to measurements far from the true value.

Key Takeaways

• The two main types of measurement error are random error and systematic error.
• Random error causes one measurement to differ slightly from the next. It comes from unpredictable changes during an experiment.
• Systematic error always affects measurements by the same amount or proportion, provided that a reading is taken the same way each time. It is predictable.
• Random errors cannot be eliminated from an experiment, but most systematic errors may be reduced.

Systematic Error Examples and Causes

Systematic error is predictable and either constant or proportional to the measurement. Systematic errors primarily influence a measurement's accuracy .

What Causes Systematic Error?

Typical causes of systematic error include observational error, imperfect instrument calibration, and environmental interference. For example:

• Forgetting to tare or zero a balance produces mass measurements that are always "off" by the same amount. An error caused by not setting an instrument to zero prior to its use is called an offset error.
• Not reading the meniscus at eye level for a volume measurement will always result in an inaccurate reading. The value will be consistently low or high, depending on whether the reading is taken from above or below the mark.
• Measuring length with a metal ruler will give a different result at a cold temperature than at a hot temperature, due to thermal expansion of the material.
• An improperly calibrated thermometer may give accurate readings within a certain temperature range, but become inaccurate at higher or lower temperatures.
• Measured distance is different using a new cloth measuring tape versus an older, stretched one. Proportional errors of this type are called scale factor errors.
• Drift occurs when successive readings become consistently lower or higher over time. Electronic equipment tends to be susceptible to drift. Many other instruments are affected by (usually positive) drift, as the device warms up.

How Can You Avoid Systematic Error?

Once its cause is identified, systematic error may be reduced to an extent. Systematic error can be minimized by routinely calibrating equipment, using controls in experiments, warming up instruments before taking readings, and comparing values against standards .

While random errors can be minimized by increasing sample size and averaging data, it's harder to compensate for systematic error. The best way to avoid systematic error is to be familiar with the limitations of instruments and experienced with their correct use.

Random Error Examples and Causes

If you take multiple measurements , the values cluster around the true value. Thus, random error primarily affects precision . Typically, random error affects the last significant digit of a measurement.

What Causes Random Error?

The main reasons for random error are limitations of instruments, environmental factors, and slight variations in procedure. For example:

• When weighing yourself on a scale, you position yourself slightly differently each time.
• When taking a volume reading in a flask, you may read the value from a different angle each time.
• Measuring the mass of a sample on an analytical balance may produce different values as air currents affect the balance or as water enters and leaves the specimen.
• Measuring your height is affected by minor posture changes.
• Measuring wind velocity depends on the height and time at which a measurement is taken. Multiple readings must be taken and averaged because gusts and changes in direction affect the value.
• Readings must be estimated when they fall between marks on a scale or when the thickness of a measurement marking is taken into account.

How Can You Avoid (or Minimize) Random Error?

Because random error always occurs and cannot be predicted , it's important to take multiple data points and average them to get a sense of the amount of variation and estimate the true value. Statistical techniques such as standard deviation can further shed light on the extent of variability within a dataset.

Cochran, W. G. (1968). "Errors of Measurement in Statistics". Technometrics. Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality. 10: 637–666. doi:10.2307/1267450

Bland, J. Martin, and Douglas G. Altman (1996). "Statistics Notes: Measurement Error." BMJ 313.7059: 744.

Taylor, J. R. (1999). An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books. p. 94. ISBN 0-935702-75-X.

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Scientific Reproducibility, Human Error, and Public Policy

One of the hallmarks of scientific progress is the ability to identify and control or reduce newly discovered sources of bias and error. In response to emerging evidence that a great deal of scientific work may not be reproducible, the National Institutes of Health recently launched a reproducibility initiative to validate data published in landmark studies. The editors of Nature and Science have also announced steps that their journals will take to promote reproducibility.

Efforts by scientific groups to promote reproducibility are focused mostly on measures aimed at reducing biases and errors related to experimental design and statistical analysis, some of which include reporting of data and methods fully, using control groups and blinding, and ensuring that studies are adequately powered. Although implementing such measures is essential to promoting reproducibility, it is also important for scientists to address bias and errors affecting their own judgment and reasoning. Taking human error seriously has significant ramifications for scientific practice, communication, and policy.

Human error and bias

Philosophers and scientists have contemplated biases that affect inquiry for hundreds of years. Francis Bacon argued in The New Organon that humans are prone to idols of the tribe (biases of the human race as a whole), idols of the den (individual biases), idols of the marketplace (errors caused by language), and idols of the theater (mistakes caused by problematic philosophical assumptions).

Psychological research over the past 40 years has shed new light on the range of biases that affect human reasoning. In work that led to a Nobel Prize in 2002, Amos Tversky and Daniel Kahneman ( 1974 ) launched research into a number of common heuristics and biases that affect judgments of probability, such as availability, anchoring, representativeness, probability neglect, and loss aversion. Importantly, subsequent studies have shown that scientists are prone to the same errors, especially when they have limited data or are forced to weigh multiple forms of evidence (Tversky and Kahneman 1986 , Hergovich et al. 2010 ).

These heuristics and biases may influence not only small-scale hypothesis choices and risk assessments but also large-scale theory change. Historians and philosophers of science have identified a range of social, personal, political, and professional factors that influenced the acceptance of theories such as continental drift, natural selection, and Mendelian genetics. For example, in accordance with the availability heuristic, geologists who had traveled and made observations in the Southern Hemisphere (where a number of phenomena were explained well by the hypothesis of continental drift) were much quicker to accept the new theory than were other geologists (Solomon 2001 ). The same was true for scientists who lived near Greenland or who studied mountain ranges—again, because prodrift data were particularly salient to them. Even birth order played a role: Firstborn scientists were half as likely to accept drift as were younger siblings. On the basis of this and other cases, Frank Sulloway ( 1996 ) argued that birth order is the most important single predictor for a scientist's likelihood of adopting major theory changes.

Other studies reveal systematic differences in scientists’ risk perceptions of phenomena such as nuclear power and industrial chemicals that are based on their gender, race, employment (academic or industry), and political ideology (Slovic 2000 ). Some of these differences may be due to the effects of defense motivation and confirmation bias, according to which people screen arguments and evidence, giving more weight and attention to considerations that support their preexisting views (Hergovich et al. 2010 ).

Studies on the influences of financial conflicts of interest on human reasoning point to a funding effect : Research results tend to be positively associated with the interests of the funders or investigators (Bekelman et al. 2003 ). Social-science research indicates that at least part of this effect is probably generated by subconscious influences on judgment and reasoning (Babcock et al. 1995 ). Studies indicate that people have a tendency to view arguments that serve their personal interests as stronger than those that do not; moreover, they significantly underestimate their own tendency to display these biases (Babcock et al. 1995 ). Research on journal peer review indicates that reviewers are affected by a number of different biases, including the author's institutional affiliation, nationality, and language and the reviewer's gender, country of origin, and relationship to the author (Lee et al. 2013 ).

Implications for science communication and policy

Taking the prevalence of human bias and error seriously leads to at least four lessons for science policy. First, scientists should explore innovative ways to disclose the range of factors that could influence their judgment and reasoning. Just as good scientists attempt to identify and address all the systemic errors and biases associated with their instruments, they should also find ways to acknowledge potential systemic errors and biases associated with their own thinking. The growing tendency to require financial conflict of interest disclosures in scientific journals is a preliminary step in this direction. Another important trend is making all study data publicly available after publication so that others can identify potential biases. When advising policymakers, scientists might take steps to acknowledge the values (e.g., protecting public health or promoting economic growth) that may have informed their conclusions (Elliott and Resnik 2014 ). They might also clarify the range of views present in the scientific community rather than advocating for a single conclusion (Elliott and Resnik 2014 ).

Second, scientists should not regard efforts to make their values and interests more transparent as a threat to their integrity or reputation. In a recent dispute over the European Commission's regulatory policy on endocrine disrupting chemicals, key scientists regarded efforts to identify their financial ties as “stupid” and a distraction from the real scientific issues at stake (Horel and Bienkowski 2013 ). This dismissive attitude seems to reflect the conviction that good scientists will not allow financial ties or other personal interests to influence their judgment. But the evidence from the social sciences indicates that most of these influences are probably subconscious. Therefore, calls for greater disclosure are not attacks on scientists’ integrity, because the influences of values are systemic effects that all scientists need to address.

Third, members of the public should welcome information about scientists’ values or interests without automatically treating this as a reason to dismiss those scientists’ input. The idea that well-founded judgments and decisions can emerge from the exchange of ideas among people with different perspectives is part of the rationale behind incorporating scientists with multiple viewpoints on advisory committees, and it is a central insight from the emerging field of social epistemology (Solomon 2001 ). In some cases, incorporating the “local expertise” of nonscientists can also help correct the blind spots of scientific experts.

Fourth, in order to promote these efforts to counterbalance and correct for errors and biases related to scientific judgment and reasoning, more research is needed in order to better understand the range of social, psychological, economic, political, and other factors that influence scientists and the best ways to control or minimize their impact.

All humans, including scientists, are prone to errors and biases related to judgment and reasoning. To advance scientific practice, communication, and policy, scientists need to pay more attention to addressing their own interests and values.

Acknowledgments

This article is the work product of an employee or group of employees of the National Institute of Environmental Health Sciences (NIEHS), part of the National Institutes of Health (NIH). However, the statements, opinions, or conclusions contained herein do not necessarily represent the statements, opinions, or conclusions of the NIEHS, the NIH, or the United States government.

References cited

• Babcock L, Loewenstein G, Issacharoff S, Camerer C. Biased judgments of fairness in bargaining. American Economic Review. 1995; 85 :1337–1342. [ Google Scholar ]
• Bekelman JE, Li Y, Gross CP. Scope and impact of financial conflicts of interest in biomedical research: A systematic review. Journal of the American Medical Association. 2003; 289 :454–465. [ PubMed ] [ Google Scholar ]
• Elliott KC, Resnik DB. Science, policy, and the transparency of values. Environmental Health Perspectives. 2014; 122 :647–650. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hergovich A, Schott R, Burger C. Biased evaluation of abstracts depending on topic and conclusion: Further evidence of a confirmation bias within scientific psychology. Current Psychology. 2010; 29 :188–209. [ Google Scholar ]
• Horel S, Bienkowski B. Special report: scientists critical of EU chemical policy have industry ties. Environmental Health News. 2013 (31 October 2014; www.environmentalhealthnews.org/ehs/news/2013/eu-conflict ) [ Google Scholar ]
• Lee CJ, Sugimoto CR, Zhang G, Cronon B. Bias in peer review. Journal of the American Society of Information Science and Technology. 2013; 64 :2–17. [ Google Scholar ]
• Slovic P. The Perception of Risk. Routledge; 2000. [ Google Scholar ]
• Solomon M. Social Empiricism. MIT Press; 2001. [ Google Scholar ]
• Sulloway F. Born to Rebel: Birth Order, Family Dynamics and Creative Lives. Pantheon Books; 1996. [ Google Scholar ]
• Tversky A, Kahneman D. Judgments under uncertainty: Heuristics and biases. Science. 1974; 185 :1124–1131. [ PubMed ] [ Google Scholar ]
• Tversky A, Kahneman D. Rational choice and the framing of decisions. Journal of Business. 1986; 59 :S251–S278. [ Google Scholar ]

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Avoiding Human Errors in Experiments for Biologics

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To some extent, errors are an inevitable part of the drug discovery and development process. In particular, working with biologics can present unique challenges, due to their complexity and sensitivity to external conditions.

But errors can be incredibly costly, wasting time and money, lowering product quality, and even resulting in noncompliance with various laws and regulations, if unaddressed. It’s estimated that errors cost an average U.S. lab about $180,000 each year in the pre- and post-analytical stages. For that reason, it’s critical to anticipate sources of human error in biologics experiments—including antibody discovery , cell-based therapies , and RNA therapies—and take steps to avoid them.

Systematic Errors

Systematic errors are flaws in an experiment’s design or procedures that shift all measurements in the same direction. As a result, they reduce the accuracy of the experiment. Examples of systematic errors include:

Calibration errors : A measurement instrument is improperly calibrated or the experimenter forgets to calibrate it. Calibration errors can also occur if equipment is not serviced periodically and maintained to a high standard.

Estimation errors : On some instruments, reading a measurement is subject to errors in human estimation. For example, viewing a meniscus from slightly different angles can lead to different recorded measurements.

Instrument drift : Instrument accuracy can change over time. For example, measurements collected from an electronic instrument may change as it warms up. Hysteresis, where a physically observable effect lags behind its cause, can also occur and should be accounted for.

Using insensitive or faulty equipment : Some measurements require more sensitivity than others. Relying on instruments with poor or inadequate sensitivity can cause experimenters to miss low-level samples.

A few key steps can help mitigate systematic errors in the lab. First, equipment should be regularly maintained and calibrated. Second, staff should be properly trained and supervised in operating instruments and recording data to minimize deviation from experimental protocols. Finally, automating as many lab activities as possible can reduce opportunities for human error (more on this below).

Random Errors

Random errors are caused by fluctuations in the experimental or measurement conditions. They tend to be small, but can still impact experimental outcomes. Examples of random errors include:

Environmental factors : Changes in temperature, light levels, and electrical or magnetic noise can all affect observation and measurements.

Transcriptional error : Transcriptional error occurs when data is recorded incorrectly.

Experimenter fatigue or inexperience : Lack of experience with equipment can cause measurements to be inaccurate or unreliable. Similarly, waning attentional resources can cause experimenter observations to decrease in accuracy over time.

Many of the human errors in this category can be reduced or eliminated by delivering proper training to staff, as well as providing sufficient breaks or alternating tasks to maintain vigilance. Conditions in the lab, including lighting and temperature, should also be monitored for consistency at regular intervals.

Decision-Making Errors

Errors in human decision-making can jeopardize an experiment from the start or introduce bias at any stage. Some common decision-making errors include:

Confirmation bias : Experimenters are less likely to detect errors in measurement if the error causes data to align with a hypothesis or desired result. They are also less likely to double-check the results of an analysis if those results support a hypothesis. Implementing extra checks, including those conducted by a disinterested party, can help experimenters avoid confirmation bias.

Experimenter bias : In any situation that involves human judgment, experimenters who are aware of the condition they are observing (control vs. treatment) can introduce unconscious bias into their measurements and decisions. Blinding a study eliminates this source of error.

The Power of Lab Automation

Lab automation offers a powerful solution for mitigating human error in biologics experiments. Relying on robotic equipment to perform various lab tasks, such as the sorting, loading, and centrifugation of specimens, can greatly reduce errors in measurement or breaches of experimental protocols.

Utilizing an ELN or informatics platform to automate various aspects of the experimental workflow can also help eliminate the bulk of human errors. Some of the ways ELNs reduce human error include:

Calibration management : An ELN with built-in calibration management software enables labs to track the status of equipment and ensure that calibration is performed in a timely manner.

Structured data entry : Allowing experimenters to enter data without predetermined parameters can result in manual data entry errors. With an ELN, predefining options for data entry cuts down on transcriptional errors.

Barcode labelling : Barcodes enable automated sample tracking and inventory management. This feature can reduce transcriptional error, as well as delays or errors in experiments resulting from depleted supplies.

Automation of lab workflows : Automating various lab activities, from note-taking to analysis, cuts down on bias and other forms of human error.

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8 Human Error Examples and Eye-Popping Sets of Statistics

On September 23, 1999, one of the greatest human errors examples in modern history made headlines across the world.

The Mars Climate Orbiter was spinning in orbit around the red planet, beginning its mission to collect data on Earth’s closest neighbor. But the Climate Orbiter went radio silent just as it was starting to orbit Mars.

As minutes of silence bled into hours, NASA technicians realized that their worst fears had come true. The Mars Climate Orbiter, all $125 million of it, was gone, likely destroyed in the punishing atmosphere of Mars.

After a comprehensive investigation, NASA found the probe failed because some of its software used United States customary units instead of the metric system.

This crucial error was somehow missed by hundreds of NASA experts, even though your average high school physics student could have diagnosed the problem.

This is just one of many human error examples at play. But every process that involves humans is inherently vulnerable to error, and that’s why sometimes bad news early is good news .

Here are eight sets of human error examples and human error statistics that will make your eyes pop.

8 Unbelievable Sets of Human Error Examples and Statistics

1. data entry error.

Data entry, with no verification layer steps, has an error rate as high as 4% . That means the error rate for data entered once, without any further verification, is 400 per 10,000 entries – a significant number that affects even small datasets.

2. The Three Mile Island Incident

The Three Mile Island Incident , the most significant accident in U.S. commercial nuclear power plant history, was caused by an erroneous human response to the partial meltdown of one of the plant’s nuclear reactors. The accident caused $2.4 billion in property damage.

3. Human Medical Error

251,454 deaths were caused by human medical error in the U.S. alone in 2016, the third leading cause of death in the nation. Doctors perform 500 incorrect surgical operations each week. 50 newborn babies are dropped at birth by doctors everyday.

4. 89th Academy Awards

At the 89th Academy Awards , a PricewaterhouseCoopers accountant broke protocol and gave out the wrong awards slip for Best Picture. The cast of  La La Land  was hurriedly rushed off the stage mid-awards speech, in a chaotic scene that was watched by millions across the world.

5. Cyber Attacks

90% of all cyber attacks  are caused by human-initiated error.  88% of all data breaches stem from human error , rather than from technical vulnerabilities. Consequently, the same study also found that “employees are unwilling to admit to their mistakes if organizations judge them severely.”

6. Airplane Accidents

Boeing says that 80% of all airplane accidents  are the result of human error. Boeing also estimates that maintenance errors cause:

• 20 to 30 percent of engine in-flight shutdowns at a cost of US$500,000 per shutdown.
• 50 percent of flight delays due to engine problems at a cost of US$9,000 per hour.
• 50 percent of flight cancellations due to engine problems at a cost of US$66,000 per cancellation.

7. Data Loss

75% of data loss  is caused by human error.  58% of service downtime  is attributable to human mistakes.  70% of all data center outages  are caused by human initiated accidents.

8. The Sinking of the Titanic

On April 15th, 1912, the RMS Titanic  sank in the North Atlantic Ocean after Captain Edward Smith misjudged the distance between an iceberg and the ship’s hull. Once deemed “unsinkable,” the ship crashed to the shock of the world, causing 1,517 deaths.

What Causes Human Error?

There are many natural and environmental factors and stressors that can cause human error. A few examples of factors that contribute to human error include emotional stress, anxiety, fatigue, distractions, complex documentation, workload, time pressure, poor communication, and much more. However, there are ways to mitigate risk and limit human error.

The Path Forward: Limiting Human Error

Human error examples and stats are often mind boggling. So what can be done to mitigate costly human mistakes, and how can we solve for the cost of human error s?

Verification steps, automation, machine learning, and artificial intelligence are fundamentally improving the accuracy of work products and processes across many sectors.

In some cases, these technologies and methods are being used in tandem with human labor to minimize human error rates. A study by UNLV found that humans empowered by automation technology are over twenty times more accurate at data verification than humans alone.

Human error isn’t going anywhere. But with technological innovation, employees, companies, and organizations are learning how to minimize its impact. And maybe that’s how organizations will avoid the next $125 million dollar mistake.

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Common sources of error in biology lab experiments

We look at what causes errors in biology lab experiments and how lab automation can help reduce them.

Errors are an unfortunate staple of laboratory life, including in biology lab experiments. Conducting experiments on biological materials can be unpredictable. Every researcher will grapple with errors to some extent, whether that be simply accounting for their presence in day to day experiments, or resenting their invalidation of a set of hard-won results.

These errors, whatever form they take, can prove to have significant consequences. It is estimated, for instance, that between 24% and 30% of laboratory errors influence patient care, and more seriously that patient harm occurs in between 3% and 12% of cases . There is also a financial dynamic, with the lost productivity and wasted resources amounting to an approximate cost of $180,000 per year in the pre and post analytical stages for laboratories in the US .

What causes errors in the lab?

In general, errors in a laboratory environment can be divided into two main groups. The first of these is systematic errors, referring to faults or flaws in the experiment design or procedure, which shift all measurements and thereby reduces the overall accuracy of an experiment. Examples here could include faulty measurement equipment, inadequate sensitivity of instruments, or calibration errors, which have the result of meaning an experiment becomes ‘biased’.

The second of these groups are random errors, which are caused by unknown and unpredictable changes in a measurement. Errors of this type impact the precision of an experiment, which in turn reduces the reproducibility of a result. There are a wide array of sources of random errors, with some examples including an experiment’s environment changing as a result of measurement, experimenter fatigue or inexperience, and even intrinsic variability.

How can you reduce the impact of errors in the lab?

Fortunately, steps can be taken to reduce the impact and occurrence of errors. When it comes to systematic errors, the main means of doing this is ensuring that experiments are carefully designed, and steps are followed attentively. Laboratories could also reduce these errors by maintaining equipment to a high standard, and ensuring staff are trained properly in its use.

Random errors are slightly harder to reduce due to their unpredictable nature, but steps can be made to reduce their impact. Most important here is ensuring the use of a large sample size and taking multiple measurements, which will ensure that random errors which occur for any reason will have only a limited bearing on the overall experiment results. On the human side, proper training is again relevant here, as is ensuring that staff take sufficient breaks.

However, using these traditional means of error reduction can only do so much, and errors continue to frustrate researchers. Given the raft of technological innovation in the laboratory space, this is a shame, particularly when it comes to such basic issues as incorrect equipment reading.

What is the solution?

One potential solution is the use of total lab automation . In practice, this would mean linking together several workstations and automating entire workflows. Automata Labs for instance links together a series of modular pods which house a robotic arm along with tried-and-tested lab equipment, and links seamlessly into broader laboratory processes.

The uses of such automation are broad, and clinical laboratory automation for instance can help produce faster and more reliable results in areas such as diagnostic and drug discovery protocols, amongst a raft of other uses.

In terms of error reduction, automation allows for taking over the bulk of many manual activities, including specimens sorting, loading, centrifugation, decapping, aliquoting, and sealing, massively reducing the risks of error in the manually-intensive pre-analytical phase. Since studies suggest that up to 70% of errors occur during this stage, this offers a hugely significant positive impact, a fact confirmed by recent polling conducted by Automata which found that 85% of lab managers associate automation with error reduction.

In the longer-term too, reducing the need for scientists to perform such manual tasks can offer a raft of positive benefits. For instance, on the health side, issues such as eye strain from tasks such as cell counting and even carpal tunnel syndrome from repeated manipulations are mitigated to a major extent.

What do lab managers think about automation’s potential to reduce human error ?

Looking more broadly, scientists working in laboratories are also simply given more time. Freeing them from mundane and manual tasks allows scientists to focus on much more high-value and intellectually demanding tasks, which permit greater creativity.

Automata recently conducted research looking at the benefits of Automation in the lab. 78% of lab managers in Automata’s recent polling emphasised the importance of this creativity angle to them, with automation correspondingly offering opportunities to improve laboratory staff productivity and morale.

But, crucially, Lab managers feel their sector is operating under significant strain. Many citied time pressures and the ability to meet current demand as key issues. Error not only compounds these problems, but human error was also specifically identified by 65% of respondents as having an impact on their work.

However, our research also identified automation is a solution. The majority of lab managers feel that full-workflow automation would have a positive impact on their day-to-day work, for example in improving capacity (93%) and turnaround time (90%). Indeed, full workflow automation was also felt to have a positive impact on human error (85%), lab safety (80%) and patient outcomes (68%).

What is laboratory workflow automation?

Laboratory workflow automation solutions link multiple processes and workflows in order to automate entire assays in diagnostic testing. In the clinical lab, laboratory workflow automation normally encompasses both lab automation hardware and software to form an integrated system that charts the end-to-end progress of a sample. This solution will automate the processes of pre-analytics, analytics and post-analytics.

A laboratory workflow automation solution in diagnostic testing may process, test and store specimens autonomously, with minimal human intervention.

With the laboratory workflow automation of diagnostics processes, human error is removed and the quality of lab results is significantly improved. You can find out more about our laboratory workflow automation solution here .

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Sources of error in lab experiments and laboratory tests

One of the major research aspects of laboratory science is physical and chemical testing; and its test findings are the primary scientific basis for assessing product quality. Physical and chemical laboratory experiments include three primary sources of error : systematic error, random error and human error . These sources of errors in lab should be studied well before any further action.

So, what are the particular sources of each error?

The reliability of physical and chemical testing has been significantly impaired; by equipment, samples, instruments, lab environment, reagents, operating procedures and other factors; leading to many errors in physical and chemical testing.

System Error in laboratory experiments

Systematic error applies to repeated measuring of the same object under repeated conditions of measurement. The amount of the error value is either positive or negative; which is called the fixed system error in laboratory experiments and laboratory tests . Or the error changes show a certain law; which is also called the variable system error, as the measurement conditions varies.

The systemic sources of error is caused primarily by:

• The incorrect method of measurement in laboratory experiments
• The incorrect method of using the instrument in laboratory experiments
• The failure of the measuring instrument in laboratory experiments
• The performance of the testing tool itself in laboratory experiments
• The inappropriate use of the standard material and the changing environmental conditions in laboratory experiments

With certain steps and proper Laboratory Equipment these sources of errors can be minimized and corrected.

Different types of system errors are:

Method error in laboratory experiments

The method error in laboratory experiments refers to the error created by the very process of physical and chemical examination. This error is inevitable so often the test result is low or high.

For example, the dissolution of the precipitate is likely to trigger errors while conducting gravimetric analysis in physical and chemical tests; there is no full reaction during the titration , or a side reaction occurs due to the incoherence of the end point of the titration with the metering level.

Instrument error in laboratory experiments

The instrument error in test labs is caused primarily by laboratory instrument inaccuracy . If the meter dial or the zero point is inaccurate, for instance; the measurement result would be too small or too big. Unless the adjustment is not done for too long, the weighing error will eventually occur. The glass gauge has not undergone standard and scale testing; so it is used after purchasing from the manufacturer, which will allow the instrument error to occur.

Reagent error in laboratory experiments

The reagent error in lab test is caused primarily by the impure reagent or the inability to meet the experimental provisions ; such as the existence of impurities in the reagent used in the physical and chemical testing phase; or the existence of contaminated water or reagent contamination that may influence the results of the examination; or the storage or operating climate. Changes in reagents and the like can cause errors in reactants.

Random Error in laboratory experiments

Error caused by various unknown factors is known as random error. This error poses erratic changes at random, primarily due to a variety of small, independent, and accidental factors. The random error is atypical from the surface. Since it is accidental, the random error is often called unmeasurable error or accidental error .

Statistical analysis can also measure random sources of error in lab, unlike systemic errors; and it can also determine the effect of random errors on the quantity or physical law under investigation. To solve random errors, scientists employ replication. Replication repeats several times a measurement, and takes the average.

Although, it should be noted that in the usual physical and chemical testing phase, which has some inevitability, both the systematic error and the random error do exist. The disparity in results caused by the inspection process mistake of the usual physical and chemical inspection personnel, incorrect addition of reagents, inaccurate procedure or reading, mistake in measurement, etc., should be considered “error” and not an error.

Thus, if there is a significant difference between repeated measurements of the same measuring object; whether it is caused by “ error ” should be considered. in such situation, the source of error in lab should be examined carefully, and its characteristics should be calculated.

An Example of some random sources of errors in lab

Example for distinguishing between systemic and random errors is; assuming you are using a stop watch to calculate the time needed for ten pendulum oscillations. One cause of error in starting and stopping the watch is your reaction time. You may start soon and stop late during one measurement; you can reverse those errors on the next.

These are accidental errors , since all cases are equally probable. Repeated tests yield a sequence of times, all slightly different. In random they differ around an average value. For example, if there is also a systemic mistake, your stop watch doesn’t start from zero; so the calculations will differ, not about the average value, but about the displaced value.

In this example both random and systemic source of errors in lab explained.

Human Error in laboratory experiments

The human error in laboratory experiments and lab tests primarily refers to the mistake in physical and chemical inspection phase caused by the factors of the inspector ; particularly in the following three aspects:

Operational error in laboratory experiments

Operational error applies to the subjective factors in regular activity of the physical and chemical inspectors. For instance, the sensitivity of the inspector to observing the color would result in errors; or there is no effective protection when weighing the sample, so that the sample is hygroscopic.

When washing the precipitate, there is an error in the absence of appropriate washing or extreme washing; Throughout the burning precipitation, did not regulate temperature; Unless the burette is not rinsed in the physical and chemical testing process before the liquid leakage, the liquid hanging phenomenon will occur which will allow the air bubbles to linger at the bottom of the burette after the liquid is injected; Inspectors looking up (or down) the scale at the time of the degree would cause errors.

Subjective error in laboratory experiments

Subjective errors are caused mainly by the subjective considerations of physical and chemical test analysts. For example, because of the difference in the degree of sharpness of color perception, some analysts believe the color is dark when the color of the titration end point is discriminated against, but some analysts think the color is brighter;

Because the angles from which the scale values are read are different, some analysts feel high while some analysts feel low in situations. Moreover, many observers would have a “pre-entry” tendency in the actual physical and chemical inspection job, that is, subjectively unconsciously biased towards the first measurement value whenever reading the second measurement value.

Negligible error in laboratory experiments

Negligible error refers to the mistake caused during the physical and chemical examination by the inspector’s reading mistake, operation error, measurement error etc. A individual can, for example, record an incorrect value, misread a scale, forget a digit while reading a scale, or record a calculation, or make a similar blunder.

Errors can lead to incorrect results , and knowing the sources of error in lab will help us mitigate error occurrence and increase test results quality.

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Human Errors in a Routine Analytical Laboratory—Classification, Modeling and Quantification: Overview of the IUPAC/CITAC Guide*

Pure and Applied Chemistry, 2016, Volume 88, Issue 5, pp. 477–515; online 22 June 2016

http://dx.doi.org/10.1515/pac-2015-1101

Human error in chemical analysis is any action or lack thereof that leads to exceeding the tolerances of the conditions required for the normative work of the measuring/testing (chemical analytical) system with which the human interacts. When the measuring system is dealing with sampling, the human may be the sampling inspector. On other steps of chemical analysis the human is the analyst/operator of the measuring system. The tolerances of the conditions are, for example, intervals of temperature and pressure values for sample decomposition, purity of reagents, pH values for an analyte extraction and separation, etc. They are formulated in a standard operation procedure (SOP) of the analysis describing the normative work, based on results of the analytical method validation study.

Human errors in a routine analytical laboratory may lead to atypical test results of questionable reliability. An important group of atypical results is out-of-specification test results — those that fall outside the established specifications in the pharmaceutical industry, or do not comply with regulatory legislation, or specification limits in other industries and fields, e.g. , environmental and food analysis.

Risk of human error is the combination of the likelihood of occurrence of the error and the severity of that error for quality of analytical results. Prevention, avoidance, or blocking of human error by a laboratory quality system is not easy, since errare humanum est (to err is human). Both correct performance and error follow from the same cognitive processes allowing us to be fast, to respond flexibly to new situations, and to juggle several tasks at once. Both are “two sides of the same theoretical coin”. An example is the “syndrome” of certified reference material (CRM), when an analyst reports an analyte concentration value close to that in a CRM certificate (applied as a control sample), which is subsequently found to be incorrect. There are a number of other human errors which may occur for various reasons. A part of them seem trivial for professionals in the analysis. However, people make trivial errors in their day-to-day life. Nobody is able to change the nature of human being. Thus, protection of the analytical result quality by managing risk of human error for reduction of the error likelihood and mitigation of its severity (the risk reduction) is an important task for the quality system of any analytical laboratory. Residual risk of human error, not prevented or blocked by the laboratory quality system, decreases quality of analytical results and can be interpreted as a source of measurement uncertainty.

There is no currently available data bank (database) containing empirical values of likelihood/frequencies of occurrence of human errors in analytical chemistry, derived from relevant operating experience, experimental research or simulation studies. On the other hand, any expert in a specific chemical analysis has necessary information accumulated during his/her work. That is why this Guide discussed classification, modeling and quantification of human errors in chemical analysis using expert judgments.

Classification

The classification includes the following nine kinds of human errors, k = 1, 2, …, K ( K = 9): seven kinds of commission errors of a sampling inspector and/or an analyst/operator (knowledge-, rule- and skill-based mistakes and routine, reasoned, reckless and malicious violations) and two kinds of omission errors (lapses and slips).

The errors may happen at any step of chemical analytical measurement/testing process, m = 1, 2, …, M (location of the error). The main steps, for example, are: 1) choice of the chemical analytical method and corresponding SOP, 2) sampling, 3) analysis of a test portion, and 4) calculation of test results and reporting. However, after sampling, a sample preparation is required in many chemical analytical methods, including the sample freezing, milling and/or decomposition. The chemical analysis may start from an analyte extraction from a test portion and separation of the analyte from other components of the extract. The analyte identification and confirmation are important in some cases. Then only calibration of the measuring system and quantification of the analyte concentration are relevant. On the other hand, choosing of an analytical method and SOP may not be necessary in a laboratory where only one method and corresponding SOP are applied for a specific task. Many chemical analytical laboratories are not responsible for sampling, etc.

The kind of human error and the step of the analysis, in which the error may happen, form the event scenario, = 1, 2, …, I . There are maximum I = K × M scenarios of human errors. Since K = 9 here, I = 9 M . These scenarios put together generate a map of human errors in chemical analysis. Mapping human errors is necessary for quality risk management of analytical results. Examples of mapping human errors in pH measurement of groundwater, multi-residue pesticide analysis of fruits and vegetables, and ICP-MS analysis of geological samples are provided in Annex A of the Guide.

A Swiss cheese model shown in Fig. 1 is used for characterizing the errors interaction with a laboratory quality system. This model considers the quality system components j = 1, 2, ..., J as protective layers against human errors. For example, the main system components are: 1) validation of the measurement/analytical method and formulation of standard operation procedures (SOP); 2) training of analysts and proficiency testing; 3) quality control using statistical charts and/or other means; and 4) supervision. Each of such components has weak points, whereby errors are not prevented, similar to holes in slices of the cheese. The presence of holes in a layer will not lead to system failure, as a rule, since other layers are able to prevent a bad outcome. That is shown in Fig. 1 as the pointers blocked by the layers. In order for an incident to occur and an atypical test result to appear, the holes in the layers must line up at the same time to permit a trajectory of incident opportunity to pass the system (through its defect), as depicted in Fig. 1 by the longest pointer. Examples of modeling human errors are available in Annex A of the Guide.

Quantification

A technique for quantifying human errors in chemical analysis using expert judgments was formulated based on the Swiss cheese model and the house-of-security approach. According to this approach, an expert may estimate likelihood p i of scenario i, by the following scale: likelihood of an unfeasible scenario as p i = 0, weak likelihood as p i = 1, medium as p i = 3, and strong (maximal) likelihood as p i = 9. The expert estimates/judgments on the severity of an error by scenario i, interpreted as the expected loss l i of quality of the analysis result, are performed on the same scale (0, 1, 3, 9). Estimates of the possible reduction r ij of the likelihood and the severity of human error scenario i as a result of the error blocking by quality system layer j (degree of interaction) are made by the same expert(s) using again the same scale. The interrelationship matrix of r ij has I rows and J columns, as shown in Fig. 1.

Blocking human error according to scenario i by a quality system component j can be more effective in presence of another component j’ ( j’ ≠ j ) because of the synergy Δ (i) jj’ between the two components. The synergy may be equal to 0 or 1 whenever the effect is absent or present, respectively. Estimates q j of importance/effectiveness of quality system component j in human error reduction are calculated as q j = Σ I i=1 p i l i r ij s ij , where the synergy factor is

Fig. 1. A laboratory quality system against human errors in the house of security. Adapted from I. Kuselman et al . , Accred. Qual. Assur. 18:459 (2013)

s ij = 1 + Σ J j' ≠ 1 Δ (i) jj’ /(J−1) .

Taking into account the synergy factor, the interrelationship matrix is to be transformed replacing r ij by r ~ = r ij s ij in every cell ij of the matrix.

This technique allows to convert the semi-intuitive expert judgments on human errors and on the laboratory quality system into the following quantitative scores expressed in %:

likelihood score of human error in the analysis P * = (100 %/9) Σ I i=1 p i /I ;

severity (loss) score of human error L* = (100 %/9) Σ I i=1 l i /I ;

effectiveness score of a component of the laboratory quality system q* j = (100 %) q j / Σ J j = 1 q j ; and

effectiveness score of the quality system, as a whole, against human error E * = (100 %/9) Σ J j = 1 q j / Σ J j = 1 Σ I i=1 p i l i s ij .

The effectiveness score of the quality system at different steps of the analysis can be evaluated also. Examples of the quantification are available in Annex A of the Guide.

Risk Evaluation of Human Errors

Since the risk of human error is a combination of the likelihood and the severity of that error, their reduction r ~ ij is the risk reduction. A score characterizing the risk reduction by the laboratory quality system in whole, expressed in %, is

r* = (100 %/18 IJ ) Σ J j = 1 Σ I i=1 r ~ ij

Then, a score of residual risk of human errors (%) which are not prevented/blocked or reduced/mitigated by the quality system, is R * = 100 % − r * . The fraction (%) of the quality of the analytical results which may be lost due to residual risk of human errors is f HE = ( P * /100 %)( L * /100 %) R * .

In practice, a quality system is not able to prevent or block human errors completely, i.e., 0 % < f HE < 100 %, and residual risk of human errors can be interpreted as a source of measurement uncertainty when human being is involved in the measurement process and human interaction with the measuring system is taken into account. Such interpretation is discussed in Annex B of the Guide. Examples of calculation of the risks, their consequences for the quality of the analytical results and corresponding contributions to the uncertainty budget are available in Annex A.

Dr. Francesca Pennecchi (at the computer) and her colleagues in their chemical lab of INRIM.

Limitations

Any expert is also a human being, and the elicitation process (by which the expert is prompted to provide error likelihood, severity and other estimates) is influenced by epistemic uncertainty, intrapersonal conflicts, etc. Therefore, evaluation of variability of the error quantification scores and relative risks due to the expert’s inherent hesitancy, is also important. A detailed analysis of the score variability, as well as the variability of the corresponding loss of quality f HE , based on Monte Carlo simulations, is presented in Annex C.

Changes in any quality system component require a re-evaluation of the quality fraction f HE of the analytical results, which may be lost due to the residual risk of human errors. Either an f HE increase ( e.g. , due to the retirement of an experienced supervisor) or a decrease ( e.g. , due to the acquisition of a new, more accurate, and more automated measuring system) is possible.

Latent errors due to a poor laboratory design, a defect in the equipment, an unsuccessful management decision not depending on the sampling inspector and/or the analyst/operator, or positive human factors are not considered in the Guide.

Implementation Remarks

Classification, modeling, and quantification of human errors in a routine laboratory show the ways for increasing the quality system effectiveness and subsequently reducing the risk of these errors in the laboratory. In particular, results of the human error study would be useful for validating the analytical method and formulation of the SOP, as well as for training and for supervision. The map of possible human error scenarios, included in the validation report, may also be useful as a checklist for the prior assessment of an analyst before assigning the task, etc.

References and Acknowledgments

The Guide of the International Union of Pure and Applied Chemistry (IUPAC) and the Cooperation on International Traceability (CITAC) was developed by the task group: I. Kuselman (Chair, Israel), F. Pennecchi (Italy), A. Fajgelj (Austria), S.L.R. Ellison (UK), Y. Karpov (Russia) and M. Epstein (Israel). The sponsoring bodies: IUPAC Analytical Chemistry Division, IUPAC Interdivisional Working Party on Harmonization of Quality Assurance, and CITAC.

The Guide includes 52 references to basic publications on human errors, ISO and JCGM documents, and articles of the task group on the topic.

The task group thanks E. Bashkansky, E. Kardash and P. Goldshlag (Israel) and W. Bich (Italy) for their help; Springer Science+Business Media ( www.springer.com ), Elsevier ( www.elsevier.com ), Bureau International des Poids et Mesures (BIPM) and IOP Publishing ( www.ioppublishing.org ) for permissions to use material from the published papers cited in the Guide.

©2016 by Walter de Gruyter Berlin/Boston

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Helicopter turboshaft engine residual life determination by neural network method.

1. Introduction

1.1. relevance of the research, 1.2. state-of-the-art, 1.3. main attributes of the research.

• Development of a mathematical model for helicopter TE residual life determination.
• Development of a neural network model for helicopter TE residual life determination.
• Development of a neural network model training algorithm for helicopter TE residual life determination.
• Conducting a computational experiment to determine helicopter TE residual life (using the example of determining TE compressor turbine blade residual life).
• Conducting a comparative analysis of the results obtained for helicopter TE residual life determination with those obtained by classical methods, based on classical statistical methods for experimental data processing (for example, the least squares method).

2. Materials and Methods

• The flexibility of including new factors (parameters) in the model allows you to integrate new factors that influence the operational status of the helicopter TE components using the function f i ( S i ). This makes it possible to take into account component degradation from various aspects, such as blade wear, compressor efficiency loss, and others, as they are researched and their impact on engine life is identified.
• The adaptability to changes in operating conditions indicates that the Δ T i coefficients in the model are adjusted for changes in operating conditions. For example, if the maintenance or operating conditions change, the factors may be revised to more accurately account for these factors.
• Neural networks allow you to customize a wear model for the specific characteristics of each engine.
• With increasing operating time, neural networks adjust parameter changes in the model until the limit value is reached, which makes it possible to adapt to the changing wear rate of components.

3. Results and Discussion

• The mean square error, as a model adequacy criterion, is a standard metric for assessing the predicted quality and models.
• Expression (19) takes into account degradation data at all available time points, which provides an overall model adequacy overview throughout its operation.
• Averaging the error over the entire sample allowed us to assess the overall adequacy of the model throughout the operation’s entire period.
• The model predicts the mean square error in real data and identifies deviations, which allows you to quickly detect and correct inaccuracies and inconsistencies.
• Expression (19) is a simple and understandable way to assess the model’s adequacy, which makes it convenient for use by engineers and equipment maintenance specialists.
• Zero expected value means that the average of all noise values is zero.
• Standard deviation σ i = 0.025 means that it characterizes the spread of noise values relative to the average value. In this case, the standard deviation was 0.025, which indicated a small spread of values.
• A uniform distribution of the power spectral density means that the noise energy is equally distributed across all frequencies.
• Neural networks solve the helicopter TE compressor turbine blade residual service life determination task more accurately than traditional methods: the identification error at the output of the developed multilayer perceptron was 4.81 times lower than that of the regression model obtained using LSM.
• The error of solving the helicopter TE compressor turbine blade residual life determination task using the developed multilayer perceptron did not exceed 0.424%; for the classical RBF network, it was 1.079%, while for the LSM, it was 2.038%.
• Neural network methods are more robust to external disturbances: for a noise level σ i = 0.025, the error in solving the helicopter TE compressor turbine blade residual life determination task when using the developed multilayer perceptron increased from 0.424 to 0.611%; for the classical RBF network—from 1.079 to 1.877%, and for the LSM—from 2.038 to 3.933%.
• The relevance of the helicopter turboshaft engine residual life determination method is substantiated, which lies in its critical role in ensuring flight reliability and safety, since timely assessment of engine conditions, taking into account many operating factors and environmental conditions, makes it possible to plan maintenance and component replacement, preventing emergencies, and increasing the resource prediction accuracy thanks to modern diagnostic systems.
• A method for determining the residual life of helicopter turboshaft engines has been developed based on a hierarchical system utilizing neural network technologies. The experimental results showed that using a multilayer perceptron within this hierarchical system yielded a maximum root-mean-square error of no more than 0.424 when applied to solving the task of estimating the residual life of the compressor turbine blades of helicopter turboshaft engines.
• Based on the backpropagation algorithm, a multilayer perceptron training algorithm has been developed, which, by introducing the initial parameter x 0 to the output layer, improved the helicopter turboshaft engine residual life prediction accuracy and use of the adaptive Adam training rate provided high accuracy (up to 99.3%) in solving the helicopter turboshaft engine compressor turbine blade residual life determination task. It has been experimentally proven that use of the developed multilayer perceptron training algorithm made it possible, with 160 training epochs, to ensure an accuracy of 99.3% and reduce losses to 0.5% in solving the helicopter turboshaft engine compressor turbine blade residual life determination task.
• Based on the helicopter turboshaft engine compressor turbine blade residual life assessment through parameter prediction and similarities with patterns from the past, a method for constructing a degradation curve has been developed. This method integrates parameter prediction and historical data analogies to enhance the accuracy of service life prediction and maintenance planning, thereby mitigating failures. Experimental validation showed that the mean square error between predicted and observed residual resource over the entire operational period did not exceed 0.0058 (0.58%), approaching zero, indicating the high adequacy of the constructed degradation curve.
• The results of solving the helicopter turboshaft engine compressor turbine blade residual life determination task using the developed multilayer perceptron as a hierarchical system were compared with the classical RBF network and the least squares method, which made it possible to reduce the first and second types of errors by 2.23 times compared with the use of the classical RBF networks, and by 4.74 times compared to using the least squares method.

4. Conclusions

Author contributions, data availability statement, conflicts of interest.

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Vladov, S.; Kovtun, V.; Sokurenko, V.; Muzychuk, O.; Vysotska, V. Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method. Electronics 2024 , 13 , 2952. https://doi.org/10.3390/electronics13152952

Vladov S, Kovtun V, Sokurenko V, Muzychuk O, Vysotska V. Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method. Electronics . 2024; 13(15):2952. https://doi.org/10.3390/electronics13152952

Vladov, Serhii, Viacheslav Kovtun, Valerii Sokurenko, Oleksandr Muzychuk, and Victoria Vysotska. 2024. "Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method" Electronics 13, no. 15: 2952. https://doi.org/10.3390/electronics13152952

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