experimental sources of error examples in chemistry

Random Error vs. Systematic Error

Two Types of Experimental Error

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

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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• Examples of Independent and Dependent Variables
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• Absolute Error or Absolute Uncertainty Definition
• Measurement Definition in Science
• The Relative Uncertainty Formula and How to Calculate It
• Sampling Error
• Scientific Method Vocabulary Terms
• Accuracy Definition in Science
• Absolute and Relative Error Calculation
• How to Measure Mass Using a Balance
• Null Hypothesis Definition and Examples
• Relative Error Definition (Science)
• Hydrometer Definition in Science

• Uncertainty, Accuracy, and Precision

Sources of Uncertainty in Measurements in the Lab

When taking a measurement or performing an experiment, there are many ways in which uncertainty can appear, even if the procedure is performed exactly as indicated. Each experiment and measurement needs to be considered carefully to identify potential limitations or tricky procedural spots.

When considering sources of error for a lab report be sure to consult with your lab manual and/or TA , as each course has different expectations on what types of error or uncertainty sources are expected to be discussed.

Types of Uncertainty or Error

While these are not sources of error, knowing the two main ways we classify uncertainty or error in a measurement may help you when considering your own experiments.

Systematic Error

Systematic errors are those that affect the accuracy of your final value. These can often be greatly reduced or eliminated entirely by adjusting your procedure. These errors usually exist and are often constant for the duration of the experiment – or if changing slightly, like an instrument reading “drifting” with time, they are in a consistent direction (higher or lower than the “true” value).

One example of a systematic error could be using a pH meter that is incorrectly calibrated, so that it reads 6.10 when immersed in a pH 6.00 buffer. Another could be doing calculations using an equilibrium constant derived for a temperature of 25.0° C when the experiment was done at 20.0° C.

Random Error

Random errors are those that primarily affect the precision of your final value. Random error can usually be reduced by adjusting the procedure or increasing skill of the experimenter, but can never be completely eliminated.

You can observe random error when you weigh an object (say, recording a mass of 1.0254 g) and when re-weighing it, you get a slightly different measurement (say 1.0255 g). Another example is the interpolation of the final digit on a scale, as in the example earlier in this section . In a group of people observing the same meniscus you expect to get a range of readings, mostly between 25.5 – 25.7 mL.

Some Common Sources of Error

Every experiment is different, but if you are analyzing your procedure for potential sources of uncertainty, there are a few places you can start:

Assumptions About Physical Status

Every procedure comes with some assumptions. Perhaps you assume that the room temperature is 25.0° C (most UCalgary building HVAC is set to 21 ° C and fluctuates around that). Maybe you assumed a typical ambient air pressure without taking a measurement of the actual value. Perhaps you further assume that physical constants, like equilibrium constants, enthalpies, and others, do not change (much) from 25.0° C to the actual ambient temperature. Perhaps you used a “literature value” rather than measuring that quantity under your own experimental conditions.

You may have also made assumptions about your reaction – that it went to completion, or that you were able to detect a colour change visually that indicated completion (but may have really been 60, 80, or 90% complete). Perhaps there is a “side reaction” that can happen, or your product was not purified or dried thoroughly in this procedure.

There are many places where assumptions (appropriate or not) appear: depending on the difference between assumed and real conditions, this may add a negligible amount of uncertainty, or even a few percent, depending on the measurement.

Limitations on Measurements

As we have seen throughout this section, every measurement has a limit – often expressed through its recorded precision or significant figures. Some equipment can be used more precisely than others: for example, a Mohr (or serological) pipet can at best be used to $\pm$ 0.1 mL precision, while a transfer (or analytical) pipet may be used to $\pm$ 0.01 mL precision.

The less-precise equipment is usually easier and faster to use, but when precision is important, be sure you have chosen the appropriate glassware or equipment for your measurement.

Limitations on Calculations

Generally, laboratory calculations reflect the precision of a measurement, rather than limiting it (or directly affecting the accuracy). However some particular points can be sources of uncertainty.

Use of physical constants can limit your accuracy or precision if you use a rounded version (e.g. $3.00\times 10^{8}$ m/s instead of 299 792 458 m/s. As discussed above, using a value that is determined for a different physical state (temperature, pressure, etc) may also introduce some error.

Creating and reading graphs can be a major source of uncertainty if done sloppily. Remember you can only read your graph as precisely as your gridlines allow : most people can accurately interpolate to 1/10 of a division at best. You may also (manually or by regression) plot a line of best fit: this line is only as good as your data, and your calculations based on it may be limited by the precision at which you have drawn or calculated this line. The video below gives some starting tips for using Excel to create a graph appropriate for a first-year chemistry laboratory report.

“To err is human” … but not all such human error is acceptable in a procedure. Some limitations are unavoidable: for example a colourblind person reading pH from a colour indicator, or a time-dependent procedure step that is tricky to complete quickly and accurately. Often, these can be designed out of a procedure, or corrected by repeating the measurement.

True mistakes along the lines of “I overfilled the volumetric flask” should be corrected in the lab if at all possible. This may be (for example) re-making the solution, or measuring the overfill to determine the true volume used in the flask. There is usually no excuse for allowing a mistake to remain in your experiment , especially if there was time to correct or repeat the measurement. If a mistake happened and you could not correct it, you should include that in your lab report – but know that it may not be enough for a complete “sources of error” discussion.

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Experimental Errors In Chemistry: Causes And Solutions

Introduction

Chemistry is a complex and demanding field that requires accuracy and precision in all aspects of experimental work. However, despite careful planning and execution, experimental errors can occur, leading to inaccurate or unreliable results. In this article, we will explore the different types of experimental errors that can occur in chemistry, their causes, and how to prevent or minimize them.

Types of Experimental Errors

Experimental errors in chemistry can be broadly categorized into two types: systematic errors and random errors. Systematic errors are caused by flaws in the experimental setup or procedures and can lead to consistently biased results. Random errors, on the other hand, are caused by chance and can occur unpredictably, leading to fluctuations in the results.

Systematic Errors

Systematic errors can be caused by a variety of factors, such as equipment calibration errors, environmental factors, or flawed experimental design. For example, if a balance used to weigh samples is not calibrated correctly, all measurements made with that balance will be consistently inaccurate. Similarly, if the temperature or humidity in the laboratory fluctuates during experiments, it can affect the results.

Random Errors

Random errors are caused by chance and can occur unpredictably. They can be caused by factors such as variations in sample size, measurement precision, or human error. For example, if a chemist accidentally spills a small amount of a reagent during a titration, it can affect the final result.

Preventing Experimental Errors

Preventing experimental errors requires careful planning and execution of experiments. Some of the measures that can be taken to prevent or minimize experimental errors include:

Equipment Calibration

All equipment used in experiments should be properly calibrated and maintained to ensure accurate measurements.

Experimental Design

Experimental design should be carefully planned to minimize the potential for errors. This includes selecting appropriate controls, replicates, and statistical analysis methods.

Training and Supervision

All researchers involved in experiments should receive proper training and supervision to ensure that they follow established protocols and procedures.

Data Analysis

Data analysis should be carefully conducted to identify and correct any errors or anomalies in the results.

Types of Error — Overview & Comparison - Expii

IB Chemistry home > Syllabus 2016 > Stoichiometry > Errors and inaccuracies in experimentation

Experimentation and measurement

Chemistry is an experimental science. All of the laws, rules and principles of chemistry have been elaborated by experiment and observation over many years.

This process is known as the experimental method and involves the following stages:

• 1 Observation of a fact pattern or principle.
• 2 Hypothesis as to the causal factors
• 3 Experiment to support the hypothesis
• 4 Repetition and duplication of the experimental results by other research groups.
• 5 General acceptance of the hypothesis.

Experimental science in schools

In principle, there are few actual measuring devices in common use in the laboratory of a normal school. Direct measurements may usually be made of the following quantities:

• Temperature
• Liquid volume

A more specialised laboratory also may have devices for measuring:

• Light absorbance

Apparatus and instrumentation

The common laboratory apparatus used to take direct measurements:

Any experiment has inherent inaccuracies that must be considered when analysing results. These inaccuracies, or errors, derive from three general sources.

• Instrumental tolerance
• Experimental design
• Human limitations

The reliability of any experimental data must take these factors into consideration. In many cases it is possible to estimate the degree of accuracy quantitatively by consideration of the percentage error in the measurements at each stage of a procedure.

Instrument tolerance

The instrumental tolerance is the degree of accuracy of a specific instrument, or piece of apparatus, being used to take a measurement. The instrument or apparatus may have the tolerance written on it, or a judgement must be made regarding the accuracy of any measurement.

For example, a thermometer may have an inherent inaccuracy of ± 0.25 ºC. This means that its accuracy lies within this range. However, it is also possible that the ability of a person to read the thermometer lies outside of this range, eg ± 0.5 ºC. The greater error margin should be used in this case.

When deciding the error of a piece of apparatus, it is aso important to take into account the number of times that a reading must be taken.

For example, a burette must be read twice to record a liquid volume - once at the start and once at the end. This means that any inaccuracy in the reading is doubled to get the inaccuracy in the volume measured. If it is only possible to measure the liquid level to an accuracy of within ± 0.05 cm 3 then the final inaccuracy in a liquid volume must be ± 0.1 cm 3 .

Error recording

The inaccuracy of any reading must be recorded in the results tables.

A typical table of results for a titration would look like this

It is clear from this table that the measurements were taken in cm 3 and that the final titre considered the inaccuracy of the two readings.

Percentage error calculation

In any procedure there are often many different kinds of measurements taken.

The simplest way to deal with errors and inaccuracy in a quantitative manner is to convert all of the estimated errors into percentage errors and to sum them for each stage of the procedure.

Using the above titration table as an example. If experiments 2 and 3 were taken to represent the average titre, then the final value would be 21.70 cm 3 ( ± 0.1 ). To convert this inaccuracy into percentage error, the absolute error (± 0.1) must be divided by the value (21.70 cm 3 ) and the whole multiplied by 100.

absolute error = ± 0.1

percentage error = ± 0.1/21.70 x 100 = ± 0.46%

Multi-stage procedures

Most experiments involve more than one operation. These are called multi-stage procedures. In order to assess the error of the final results of an experiment, the inaccuracies at each stage of the procedure must be taken into account. To do this the individual measurement errors are normally converted into percentage errors.

These can be summed to give a final percentage error that, in turn, is re-converted into an absolute error, or inaccuracy, in the final answer.

Error analysis

Tolerance of electronic balance = ± 0.005 g

percentage error in mass = 0.005/5.20 x 100 = 0.096%

Tolerance of volumetric flask = ± 0.23 cm 3

percentage error in volumetric flask solution = 0.23/250 x 100 = 0.092%

Tolerance of pipette = ± 0.04 cm 3

percentage error in pipette = 0.04/25 x 100 = 0.160%

Tolerance of burette = ± 0.1 cm 3

Percentage error in burette = 0.1/21.7 x 100 = 0.461%

Total percentage error in titration 0.096 + 0.092 + 0.160 + 0.461 = 0.809%

It is this final error percentage that must be used to calculate the absolute error in the unknown solution concentration.

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Reasons for Error in a Chemistry Experiment

Errors in Titration Experiments

To a scientist, the definition of "error" is, in some cases, different from the normal use of this term. An error in chemistry still often means a mistake, such as reading a scale incorrectly, but it is also the normal, unavoidable inaccuracies associated with measurements in a lab. Using this expanded definition, there are many different sources of error in an experiment or scientific process.

Human Error

A few errors in chemistry experiments are due simply to mistakes on the part of the person performing the work. There are an endless number of potential mistakes in lab work, but some of the most common include misreading gauges, making math mistakes during dilutions and other types of calculations and spilling chemicals during transfer. Depending on the type of mistake and the stage at which it happens, the associated degree of error in the experimental results will vary widely in magnitude.

Improper Calibrations

Incorrect or non-existent calibration of instruments is another common source of error in chemistry. Calibration is the process of adjusting or checking an instrument to ensure that the readings it gives are accurate. To calibrate a weigh scale, for example, you might place an object known to weigh 10 grams on the scale, then check that the scale reads 10 grams. Instruments which are not calibrated or are improperly calibrated are not uncommon in chemical labs and lead to wrong results.

Measurement Estimation

In the expanded meaning of "error" in science, the process of estimating a measurement is considered a source of error. For example, a technician filling a beaker with water to a given volume has to watch the water level and stop when it is level with the filling line marked on the container. Unavoidably, even the most careful technician will sometimes be slightly over or below the mark even if only by a very small amount. Similar errors also occur in other circumstances, such as when estimating the end point of a reaction by looking for a specific color change in the reacting chemicals.

Measurement Device Limitations

Chemists also consider the limitations of measurement equipment in a lab as a source of error. Every instrument or device, no matter how accurate, will have some degree of imprecision associated with it. For example, a measuring flask is provided by the manufacturer with an acknowledged imprecision of from 1 to 5 percent. Using this glassware to make measurements in a lab therefore introduces an error based on that imprecision. In the same manner, other instruments such as weigh scales also have inherent imprecision that unavoidably causes some error.

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• Michigan Technological University: Error Analysis: Physical Chemistry Laboratory

About the Author

Michael Judge has been writing for over a decade and has been published in "The Globe and Mail" (Canada's national newspaper) and the U.K. magazine "New Scientist." He holds a Master of Science from the University of Waterloo. Michael has worked for an aerospace firm where he was in charge of rocket propellant formulation and is now a college instructor.

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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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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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4.2: Characterizing Experimental Errors

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• Felixtrument
• Laboratory Equipment

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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The chemistry of errors

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