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Sources of Error in Science Experiments

Science labs usually ask you to compare your results against theoretical or known values. This helps you evaluate your results and compare them against other people’s values. The difference between your results and the expected or theoretical results is called error. The amount of error that is acceptable depends on the experiment, but a margin of error of 10% is generally considered acceptable. If there is a large margin of error, you’ll be asked to go over your procedure and identify any mistakes you may have made or places where error might have been introduced. So, you need to know the different types and sources of error and how to calculate them.

How to Calculate Absolute Error

One method of measuring error is by calculating absolute error , which is also called absolute uncertainty. This measure of accuracy is reported using the units of measurement. Absolute error is simply the difference between the measured value and either the true value or the average value of the data.

absolute error = measured value – true value

For example, if you measure gravity to be 9.6 m/s 2 and the true value is 9.8 m/s 2 , then the absolute error of the measurement is 0.2 m/s 2 . You could report the error with a sign, so the absolute error in this example could be -0.2 m/s 2 .

If you measure the length of a sample three times and get 1.1 cm, 1.5 cm, and 1.3 cm, then the absolute error is +/- 0.2 cm or you would say the length of the sample is 1.3 cm (the average) +/- 0.2 cm.

Some people consider absolute error to be a measure of how accurate your measuring instrument is. If you are using a ruler that reports length to the nearest millimeter, you might say the absolute error of any measurement taken with that ruler is to the nearest 1 mm or (if you feel confident you can see between one mark and the next) to the nearest 0.5 mm.

How to Calculate Relative Error

Relative error is based on the absolute error value. It compares how large the error is to the magnitude of the measurement. So, an error of 0.1 kg might be insignificant when weighing a person, but pretty terrible when weighing a apple. Relative error is a fraction, decimal value, or percent.

Relative Error = Absolute Error / Total Value

For example, if your speedometer says you are going 55 mph, when you’re really going 58 mph, the absolute error is 3 mph / 58 mph or 0.05, which you could multiple by 100% to give 5%. Relative error may be reported with a sign. In this case, the speedometer is off by -5% because the recorded value is lower than the true value.

Because the absolute error definition is ambiguous, most lab reports ask for percent error or percent difference.

How to Calculate Percent Error

The most common error calculation is percent error , which is used when comparing your results against a known, theoretical, or accepted value. As you probably guess from the name, percent error is expressed as a percentage. It is the absolute (no negative sign) difference between your value and the accepted value, divided by the accepted value, multiplied by 100% to give the percent:

% error = [accepted – experimental ] / accepted x 100%

How to Calculate Percent Difference

Another common error calculation is called percent difference . It is used when you are comparing one experimental result to another. In this case, no result is necessarily better than another, so the percent difference is the absolute value (no negative sign) of the difference between the values, divided by the average of the two numbers, multiplied by 100% to give a percentage:

% difference = [experimental value – other value] / average x 100%

Sources and Types of Error

Every experimental measurement, no matter how carefully you take it, contains some amount of uncertainty or error. You are measuring against a standard, using an instrument that can never perfectly duplicate the standard, plus you’re human, so you might introduce errors based on your technique. The three main categories of errors are systematic errors, random errors , and personal errors. Here’s what these types of errors are and common examples.

Systematic Errors

Systematic error affects all the measurements you take. All of these errors will be in the same direction (greater than or less than the true value) and you can’t compensate for them by taking additional data. Examples of Systematic Errors

• If you forget to calibrate a balance or you’re off a bit in the calibration, all mass measurements will be high/low by the same amount. Some instruments require periodic calibration throughout the course of an experiment , so it’s good to make a note in your lab notebook to see whether the calibrations appears to have affected the data.
• Another example is measuring volume by reading a meniscus (parallax). You likely read a meniscus exactly the same way each time, but it’s never perfectly correct. Another person taking the reading may take the same reading, but view the meniscus from a different angle, thus getting a different result. Parallax can occur in other types of optical measurements, such as those taken with a microscope or telescope.
• Instrument drift is a common source of error when using electronic instruments. As the instruments warm up, the measurements may change. Other common systematic errors include hysteresis or lag time, either relating to instrument response to a change in conditions or relating to fluctuations in an instrument that hasn’t reached equilibrium. Note some of these systematic errors are progressive, so data becomes better (or worse) over time, so it’s hard to compare data points taken at the beginning of an experiment with those taken at the end. This is why it’s a good idea to record data sequentially, so you can spot gradual trends if they occur. This is also why it’s good to take data starting with different specimens each time (if applicable), rather than always following the same sequence.
• Not accounting for a variable that turns out to be important is usually a systematic error, although it could be a random error or a confounding variable. If you find an influencing factor, it’s worth noting in a report and may lead to further experimentation after isolating and controlling this variable.

Random Errors

Random errors are due to fluctuations in the experimental or measurement conditions. Usually these errors are small. Taking more data tends to reduce the effect of random errors. Examples of Random Errors

• If your experiment requires stable conditions, but a large group of people stomp through the room during one data set, random error will be introduced. Drafts, temperature changes, light/dark differences, and electrical or magnetic noise are all examples of environmental factors that can introduce random errors.
• Physical errors may also occur, since a sample is never completely homogeneous. For this reason, it’s best to test using different locations of a sample or take multiple measurements to reduce the amount of error.
• Instrument resolution is also considered a type of random error because the measurement is equally likely higher or lower than the true value. An example of a resolution error is taking volume measurements with a beaker as opposed to a graduated cylinder. The beaker will have a greater amount of error than the cylinder.
• Incomplete definition can be a systematic or random error, depending on the circumstances. What incomplete definition means is that it can be hard for two people to define the point at which the measurement is complete. For example, if you’re measuring length with an elastic string, you’ll need to decide with your peers when the string is tight enough without stretching it. During a titration, if you’re looking for a color change, it can be hard to tell when it actually occurs.

Personal Errors

When writing a lab report, you shouldn’t cite “human error” as a source of error. Rather, you should attempt to identify a specific mistake or problem. One common personal error is going into an experiment with a bias about whether a hypothesis will be supported or rejects. Another common personal error is lack of experience with a piece of equipment, where your measurements may become more accurate and reliable after you know what you’re doing. Another type of personal error is a simple mistake, where you might have used an incorrect quantity of a chemical, timed an experiment inconsistently, or skipped a step in a protocol.

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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.

• 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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Validity, Accuracy and Reliability Explained with Examples

This is part of the NSW HSC science curriculum part of the Working Scientifically skills.

Part 1 – Validity

Part 2 – Accuracy

Part 3 – Reliability

Science experiments are an essential part of high school education, helping students understand key concepts and develop critical thinking skills. However, the value of an experiment lies in its validity, accuracy, and reliability. Let's break down these terms and explore how they can be improved and reduced, using simple experiments as examples.

Target Analogy to Understand Accuracy and Reliability

The target analogy is a classic way to understand the concepts of accuracy and reliability in scientific measurements and experiments. 

Accuracy refers to how close a measurement is to the true or accepted value. In the analogy, it's how close the arrows come to hitting the bullseye (represents the true or accepted value).

Reliability  refers to the consistency of a set of measurements. Reliable data can be reproduced under the same conditions. In the analogy, it's represented by how tightly the arrows are grouped together, regardless of whether they hit the bullseye. Therefore, we can have scientific results that are reliable but inaccurate.

• Validity  refers to how well an experiment investigates the aim or tests the underlying hypothesis. While validity is not represented in this target analogy, the validity of an experiment can sometimes be assessed by using the accuracy of results as a proxy. Experiments that produce accurate results are likely to be valid as invalid experiments usually do not yield accurate result.

Validity refers to how well an experiment measures what it is supposed to measure and investigates the aim.

Ask yourself the questions:

• "Is my experimental method and design suitable?"
• "Is my experiment testing or investigating what it's suppose to?"

For example, if you're investigating the effect of the volume of water (independent variable) on plant growth, your experiment would be valid if you measure growth factors like height or leaf size (these would be your dependent variables).

However, validity entails more than just what's being measured. When assessing validity, you should also examine how well the experimental methodology investigates the aim of the experiment.

Assessing Validity

An experiment’s procedure, the subsequent methods of analysis of the data, the data itself, and the conclusion you draw from the data, all have their own associated validities. It is important to understand this division because there are different factors to consider when assessing the validity of any single one of them. The validity of an experiment as a whole , depends on the individual validities of these components.

When assessing the validity of the procedure , consider the following:

• Does the procedure control all necessary variables except for the dependent and independent variables? That is, have you isolated the effect of the independent variable on the dependent variable?
• Does this effect you have isolated actually address the aim and/or hypothesis?
• Does your method include enough repetitions for a reliable result? (Read more about reliability below)

When assessing the validity of the method of analysis of the data , consider the following:

• Does the analysis extrapolate or interpolate the experimental data? Generally, interpolation is valid, but extrapolation is invalid. This because by extrapolating, you are ‘peering out into the darkness’ – just because your data showed a certain trend for a certain range it does not mean that this trend will hold for all.
• Does the analysis use accepted laws and mathematical relationships? That is, do the equations used for analysis have scientific or mathematical base? For example, `F = ma` is an accepted law in physics, but if in the analysis you made up a relationship like `F = ma^2` that has no scientific or mathematical backing, the method of analysis is invalid.
• Is the most appropriate method of analysis used? Consider the differences between using a table and a graph. In a graph, you can use the gradient to minimise the effects of systematic errors and can also reduce the effect of random errors. The visual nature of a graph also allows you to easily identify outliers and potentially exclude them from analysis. This is why graphical analysis is generally more valid than using values from tables.

When assessing the validity of your results , consider the following: 

• Is your primary data (data you collected from your own experiment) BOTH accurate and reliable? If not, it is invalid.
• Are the secondary sources you may have used BOTH reliable and accurate?

When assessing the validity of your conclusion , consider the following:

• Does your conclusion relate directly to the aim or the hypothesis?

How to Improve Validity

Ways of improving validity will differ across experiments. You must first identify what area(s) of the experiment’s validity is lacking (is it the procedure, analysis, results, or conclusion?). Then, you must come up with ways of overcoming the particular weakness. 

Below are some examples of this.

Example – Validity in Chemistry Experiment 

Let's say we want to measure the mass of carbon dioxide in a can of soft drink.

The following steps are followed:

• Weigh an unopened can of soft drink on an electronic balance.
• Open the can.
• Place the can on a hot plate until it begins to boil.
• When cool, re-weigh the can to determine the mass loss.

To ensure this experiment is valid, we must establish controlled variables:

• type of soft drink used
• temperature at which this experiment is conducted
• period of time before soft drink is re-weighed

Despite these controlled variables, this experiment is invalid because it actually doesn't help us measure the mass of carbon dioxide in the soft drink. This is because by heating the soft drink until it boils, we are also losing water due to evaporation. As a result, the mass loss measured is not only due to the loss of carbon dioxide, but also water. A simple way to improve the validity of this experiment is to not heat it; by simply opening the can of soft drink, carbon dioxide in the can will escape without loss of water.

Example – Validity in Physics Experiment

Let's say we want to measure the value of gravitational acceleration `g` using a simple pendulum system, and the following equation:

$$T = 2\pi \sqrt{\frac{l}{g}}$$

• `T` is the period of oscillation
• `l` is the length of string attached to the mass
• `g` is the acceleration due to gravity

• Cut a piece of a string or dental floss so that it is 1.0 m long.
• Attach a 500.0 g mass of high density to the end of the string.
• Attach the other end of the string to the retort stand using a clamp.
• Starting at an angle of less than 10º, allow the pendulum to swing and measure the pendulum’s period for 10 oscillations using a stopwatch.
• Repeat the experiment with 1.2 m, 1.5 m and 1.8 m strings.

The controlled variables we must established in this experiment include:

• mass used in the pendulum
• location at which the experiment is conducted

The validity of this experiment depends on the starting angle of oscillation. The above equation (method of analysis) is only true for small angles (`\theta < 15^{\circ}`) such that `\sin \theta = \theta`. We also want to make sure the pendulum system has a small enough surface area to minimise the effect of air resistance on its oscillation.

In this instance, it would be invalid to use a pair of values (length and period) to calculate the value of gravitational acceleration. A more appropriate method of analysis would be to plot the length and period squared to obtain a linear relationship, then use the value of the gradient of the line of best fit to determine the value of `g`. 

Accuracy refers to how close the experimental measurements are to the true value.

Accuracy depends on

• the validity of the experiment
• the degree of error:
• systematic errors are those that are systemic in your experiment. That is, they effect every single one of your data points consistently, meaning that the cause of the error is always present. For example, it could be a badly calibrated temperature gauge that reports every reading 5 °C above the true value.
• random errors are errors that occur inconsistently. For example, the temperature gauge readings might be affected by random fluctuations in room temperature. Some readings might be above the true value, some might then be below the true value.
• sensitivity of equipment used.

Assessing Accuracy 

The effect of errors and insensitive equipment can both be captured by calculating the percentage error:

$$\text{% error} = \frac{\text{|experimental value – true value|}}{\text{true value}} \times 100%$$

Generally, measurements are considered accurate when the percentage error is less than 5%. You should always take the context of the experimental into account when assessing accuracy. 

While accuracy and validity have different definitions, the two are closely related. Accurate results often suggest that the underlying experiment is valid, as invalid experiments are unlikely to produce accurate results.

In a simple pendulum experiment, if your measurements of the pendulum's period are close to the calculated value, your experiment is accurate. A table showing sample experimental measurements vs accepted values from using the equation above is shown below. 

All experimental values in the table above are within 5% of accepted (theoretical) values, they are therefore considered as accurate. 

How to Improve Accuracy

• Remove systematic errors : for example, if the experiment’s measuring instruments are poorly calibrated, then you should correctly calibrate it before doing the experiment again.
• Reduce the influence of random errors : this can be done by having more repetitions in the experiment and reporting the average values. This is because if you have enough of these random errors – some above the true value and some below the true value – then averaging them will make them cancel each other out This brings your average value closer and closer to the true value.
• Use More Sensitive Equipments: For example, use a recording to measure time by analysing motion of an object frame by frame, instead of using a stopwatch. The sensitivity of an equipment can be measured by the limit of reading . For example, stopwatches may only measure to the nearest millisecond – that is their limit of reading. But recordings can be analysed to the frame. And, depending on the frame rate of the camera, this could mean measuring to the nearest microsecond.
• Obtain More Measurements and Over a Wider Range:  In some cases, the relationship between two variables can be more accurately determined by testing over a wider range. For example, in the pendulum experiment, periods when strings of various lengths are used can be measured. In this instance, repeating the experiment does not relate to reliability because we have changed the value of the independent variable tested.

Reliability

Reliability involves the consistency of your results over multiple trials.

Assessing Reliability

The reliability of an experiment can be broken down into the reliability of the procedure and the reliability of the final results.

The reliability of the procedure refers to how consistently the steps of your experiment produce similar results. For example, if an experiment produces the same values every time it is repeated, then it is highly reliable. This can be assessed quantitatively by looking at the spread of measurements, using statistical tests such as greatest deviation from the mean, standard deviations, or z-scores.

Ask yourself: "Is my result reproducible?"

The reliability of results cannot be assessed if there is only one data point or measurement obtained in the experiment. There must be at least 3. When you're repeating the experiment to assess the reliability of its results, you must follow the  same steps , use the  same value  for the independent variable. Results obtained from methods with different steps cannot be assessed for their reliability.

Obtaining only one measurement in an experiment is not enough because it could be affected by errors and have been produced due to pure chance. Repeating the experiment and obtaining the same or similar results will increase your confidence that the results are reproducible (therefore reliable).

In the soft drink experiment, reliability can be assessed by repeating the steps at least three times:

The mass loss measured in all three trials are fairly consistent, suggesting that the reliability of the underly method is high.

The reliability of the final results refers to how consistently your final data points (e.g. average value of repeated trials) point towards the same trend. That is, how close are they all to the trend line? This can be assessed quantitatively using the `R^2` value. `R^2` value ranges between 0 and 1, a value of 0 suggests there is no correlation between data points, and a value of 1 suggests a perfect correlation with no variance from trend line.

In the pendulum experiment, we can calculate the `R^2` value (done in Excel) by using the final average period values measured for each pendulum length.

Here, a `R^2` value of 0.9758 suggests the four average values are fairly close to the overall linear trend line (low variance from trend line). Thus, the results are fairly reliable. 

How to Improve Reliability

A common misconception is that increasing the number of trials increases the reliability of the procedure . This is not true. The only way to increase the reliability of the procedure is to revise it. This could mean using instruments that are less susceptible to random errors, which cause measurements to be more variable.

Increasing the number of trials actually increases the reliability of the final results . This is because having more repetitions reduces the influence of random errors and brings the average values closer to the true values. Generally, the closer experimental values are to true values, the closer they are to the true trend. That is, accurate data points are generally reliable and all point towards the same trend.

Reliable but Inaccurate / Invalid

It is important to understand that results from an experiment can be reliable (consistent), but inaccurate (deviate greatly from theoretical values) and/or invalid. In this case, your procedure  is reliable, but your final results likely are not.

Examples of Reliability

Using the soft drink example again, if the mass losses measured for three soft drinks (same brand and type of drink) are consistent, then it's reliable. 

Using the pendulum example again, if you get similar period measurements every time you repeat the experiment, it’s reliable.  

However, in both cases, if the underlying methods are invalid, the consistent results would be invalid and inaccurate (despite being reliable).

Do you have trouble understanding validity, accuracy or reliability in your science experiment or depth study?

Consider getting personalised help from our 1-on-1 mentoring program !

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Experimentation in Scientific Research: Variables and controls in practice

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that experimental design was developed more than a thousand years ago by a Middle Eastern scientist who studied light? All of us use a form of experimental research in our day to day lives when we try to find the spot with the best cell phone reception, try out new cooking recipes, and more. Scientific experiments are built on similar principles.

Experimentation is a research method in which one or more variables are consciously manipulated and the outcome or effect of that manipulation on other variables is observed.

Experimental designs often make use of controls that provide a measure of variability within a system and a check for sources of error.

Experimental methods are commonly applied to determine causal relationships or to quantify the magnitude of response of a variable.

Anyone who has used a cellular phone knows that certain situations require a bit of research: If you suddenly find yourself in an area with poor phone reception, you might move a bit to the left or right, walk a few steps forward or back, or even hold the phone over your head to get a better signal. While the actions of a cell phone user might seem obvious, the person seeking cell phone reception is actually performing a scientific experiment: consciously manipulating one component (the location of the cell phone) and observing the effect of that action on another component (the phone's reception). Scientific experiments are obviously a bit more complicated, and generally involve more rigorous use of controls , but they draw on the same type of reasoning that we use in many everyday situations. In fact, the earliest documented scientific experiments were devised to answer a very common everyday question: how vision works.

• A brief history of experimental methods

Figure 1: Alhazen (965-ca.1039) as pictured on an Iraqi 10,000-dinar note

One of the first ideas regarding how human vision works came from the Greek philosopher Empedocles around 450 BCE . Empedocles reasoned that the Greek goddess Aphrodite had lit a fire in the human eye, and vision was possible because light rays from this fire emanated from the eye, illuminating objects around us. While a number of people challenged this proposal, the idea that light radiated from the human eye proved surprisingly persistent until around 1,000 CE , when a Middle Eastern scientist advanced our knowledge of the nature of light and, in so doing, developed a new and more rigorous approach to scientific research . Abū 'Alī al-Hasan ibn al-Hasan ibn al-Haytham, also known as Alhazen , was born in 965 CE in the Arabian city of Basra in what is present-day Iraq. He began his scientific studies in physics, mathematics, and other sciences after reading the works of several Greek philosophers. One of Alhazen's most significant contributions was a seven-volume work on optics titled Kitab al-Manazir (later translated to Latin as Opticae Thesaurus Alhazeni – Alhazen's Book of Optics ). Beyond the contributions this book made to the field of optics, it was a remarkable work in that it based conclusions on experimental evidence rather than abstract reasoning – the first major publication to do so. Alhazen's contributions have proved so significant that his likeness was immortalized on the 2003 10,000-dinar note issued by Iraq (Figure 1).

Alhazen invested significant time studying light , color, shadows, rainbows, and other optical phenomena. Among this work was a study in which he stood in a darkened room with a small hole in one wall. Outside of the room, he hung two lanterns at different heights. Alhazen observed that the light from each lantern illuminated a different spot in the room, and each lighted spot formed a direct line with the hole and one of the lanterns outside the room. He also found that covering a lantern caused the spot it illuminated to darken, and exposing the lantern caused the spot to reappear. Thus, Alhazen provided some of the first experimental evidence that light does not emanate from the human eye but rather is emitted by certain objects (like lanterns) and travels from these objects in straight lines. Alhazen's experiment may seem simplistic today, but his methodology was groundbreaking: He developed a hypothesis based on observations of physical relationships (that light comes from objects), and then designed an experiment to test that hypothesis. Despite the simplicity of the method , Alhazen's experiment was a critical step in refuting the long-standing theory that light emanated from the human eye, and it was a major event in the development of modern scientific research methodology.

Comprehension Checkpoint

• Experimentation as a scientific research method

Experimentation is one scientific research method , perhaps the most recognizable, in a spectrum of methods that also includes description, comparison, and modeling (see our Description , Comparison , and Modeling modules). While all of these methods share in common a scientific approach, experimentation is unique in that it involves the conscious manipulation of certain aspects of a real system and the observation of the effects of that manipulation. You could solve a cell phone reception problem by walking around a neighborhood until you see a cell phone tower, observing other cell phone users to see where those people who get the best reception are standing, or looking on the web for a map of cell phone signal coverage. All of these methods could also provide answers, but by moving around and testing reception yourself, you are experimenting.

• Variables: Independent and dependent

In the experimental method , a condition or a parameter , generally referred to as a variable , is consciously manipulated (often referred to as a treatment) and the outcome or effect of that manipulation is observed on other variables. Variables are given different names depending on whether they are the ones manipulated or the ones observed:

• Independent variable refers to a condition within an experiment that is manipulated by the scientist.
• Dependent variable refers to an event or outcome of an experiment that might be affected by the manipulation of the independent variable .

Scientific experimentation helps to determine the nature of the relationship between independent and dependent variables . While it is often difficult, or sometimes impossible, to manipulate a single variable in an experiment , scientists often work to minimize the number of variables being manipulated. For example, as we move from one location to another to get better cell reception, we likely change the orientation of our body, perhaps from south-facing to east-facing, or we hold the cell phone at a different angle. Which variable affected reception: location, orientation, or angle of the phone? It is critical that scientists understand which aspects of their experiment they are manipulating so that they can accurately determine the impacts of that manipulation . In order to constrain the possible outcomes of an experimental procedure, most scientific experiments use a system of controls .

• Controls: Negative, positive, and placebos

In a controlled study, a scientist essentially runs two (or more) parallel and simultaneous experiments: a treatment group, in which the effect of an experimental manipulation is observed on a dependent variable , and a control group, which uses all of the same conditions as the first with the exception of the actual treatment. Controls can fall into one of two groups: negative controls and positive controls .

In a negative control , the control group is exposed to all of the experimental conditions except for the actual treatment . The need to match all experimental conditions exactly is so great that, for example, in a trial for a new drug, the negative control group will be given a pill or liquid that looks exactly like the drug, except that it will not contain the drug itself, a control often referred to as a placebo . Negative controls allow scientists to measure the natural variability of the dependent variable(s), provide a means of measuring error in the experiment , and also provide a baseline to measure against the experimental treatment.

Some experimental designs also make use of positive controls . A positive control is run as a parallel experiment and generally involves the use of an alternative treatment that the researcher knows will have an effect on the dependent variable . For example, when testing the effectiveness of a new drug for pain relief, a scientist might administer treatment placebo to one group of patients as a negative control , and a known treatment like aspirin to a separate group of individuals as a positive control since the pain-relieving aspects of aspirin are well documented. In both cases, the controls allow scientists to quantify background variability and reject alternative hypotheses that might otherwise explain the effect of the treatment on the dependent variable .

• Experimentation in practice: The case of Louis Pasteur

Well-controlled experiments generally provide strong evidence of causality, demonstrating whether the manipulation of one variable causes a response in another variable. For example, as early as the 6th century BCE , Anaximander , a Greek philosopher, speculated that life could be formed from a mixture of sea water, mud, and sunlight. The idea probably stemmed from the observation of worms, mosquitoes, and other insects "magically" appearing in mudflats and other shallow areas. While the suggestion was challenged on a number of occasions, the idea that living microorganisms could be spontaneously generated from air persisted until the middle of the 18 th century.

In the 1750s, John Needham, a Scottish clergyman and naturalist, claimed to have proved that spontaneous generation does occur when he showed that microorganisms flourished in certain foods such as soup broth, even after they had been briefly boiled and covered. Several years later, the Italian abbot and biologist Lazzaro Spallanzani , boiled soup broth for over an hour and then placed bowls of this soup in different conditions, sealing some and leaving others exposed to air. Spallanzani found that microorganisms grew in the soup exposed to air but were absent from the sealed soup. He therefore challenged Needham's conclusions and hypothesized that microorganisms suspended in air settled onto the exposed soup but not the sealed soup, and rejected the idea of spontaneous generation .

Needham countered, arguing that the growth of bacteria in the soup was not due to microbes settling onto the soup from the air, but rather because spontaneous generation required contact with an intangible "life force" in the air itself. He proposed that Spallanzani's extensive boiling destroyed the "life force" present in the soup, preventing spontaneous generation in the sealed bowls but allowing air to replenish the life force in the open bowls. For several decades, scientists continued to debate the spontaneous generation theory of life, with support for the theory coming from several notable scientists including Félix Pouchet and Henry Bastion. Pouchet, Director of the Rouen Museum of Natural History in France, and Bastion, a well-known British bacteriologist, argued that living organisms could spontaneously arise from chemical processes such as fermentation and putrefaction. The debate became so heated that in 1860, the French Academy of Sciences established the Alhumbert prize of 2,500 francs to the first person who could conclusively resolve the conflict. In 1864, Louis Pasteur achieved that result with a series of well-controlled experiments and in doing so claimed the Alhumbert prize.

Pasteur prepared for his experiments by studying the work of others that came before him. In fact, in April 1861 Pasteur wrote to Pouchet to obtain a research description that Pouchet had published. In this letter, Pasteur writes:

Paris, April 3, 1861 Dear Colleague, The difference of our opinions on the famous question of spontaneous generation does not prevent me from esteeming highly your labor and praiseworthy efforts... The sincerity of these sentiments...permits me to have recourse to your obligingness in full confidence. I read with great care everything that you write on the subject that occupies both of us. Now, I cannot obtain a brochure that I understand you have just published.... I would be happy to have a copy of it because I am at present editing the totality of my observations, where naturally I criticize your assertions. L. Pasteur (Porter, 1961)

Pasteur received the brochure from Pouchet several days later and went on to conduct his own experiments . In these, he repeated Spallanzani's method of boiling soup broth, but he divided the broth into portions and exposed these portions to different controlled conditions. Some broth was placed in flasks that had straight necks that were open to the air, some broth was placed in sealed flasks that were not open to the air, and some broth was placed into a specially designed set of swan-necked flasks, in which the broth would be open to the air but the air would have to travel a curved path before reaching the broth, thus preventing anything that might be present in the air from simply settling onto the soup (Figure 2). Pasteur then observed the response of the dependent variable (the growth of microorganisms) in response to the independent variable (the design of the flask). Pasteur's experiments contained both positive controls (samples in the straight-necked flasks that he knew would become contaminated with microorganisms) and negative controls (samples in the sealed flasks that he knew would remain sterile). If spontaneous generation did indeed occur upon exposure to air, Pasteur hypothesized, microorganisms would be found in both the swan-neck flasks and the straight-necked flasks, but not in the sealed flasks. Instead, Pasteur found that microorganisms appeared in the straight-necked flasks, but not in the sealed flasks or the swan-necked flasks.

Figure 2: Pasteur's drawings of the flasks he used (Pasteur, 1861). Fig. 25 D, C, and B (top) show various sealed flasks (negative controls); Fig. 26 (bottom right) illustrates a straight-necked flask directly open to the atmosphere (positive control); and Fig. 25 A (bottom left) illustrates the specially designed swan-necked flask (treatment group).

By using controls and replicating his experiment (he used more than one of each type of flask), Pasteur was able to answer many of the questions that still surrounded the issue of spontaneous generation. Pasteur said of his experimental design, "I affirm with the most perfect sincerity that I have never had a single experiment, arranged as I have just explained, which gave me a doubtful result" (Porter, 1961). Pasteur's work helped refute the theory of spontaneous generation – his experiments showed that air alone was not the cause of bacterial growth in the flask, and his research supported the hypothesis that live microorganisms suspended in air could settle onto the broth in open-necked flasks via gravity .

• Experimentation across disciplines

Experiments are used across all scientific disciplines to investigate a multitude of questions. In some cases, scientific experiments are used for exploratory purposes in which the scientist does not know what the dependent variable is. In this type of experiment, the scientist will manipulate an independent variable and observe what the effect of the manipulation is in order to identify a dependent variable (or variables). Exploratory experiments are sometimes used in nutritional biology when scientists probe the function and purpose of dietary nutrients . In one approach, a scientist will expose one group of animals to a normal diet, and a second group to a similar diet except that it is lacking a specific vitamin or nutrient. The researcher will then observe the two groups to see what specific physiological changes or medical problems arise in the group lacking the nutrient being studied.

Scientific experiments are also commonly used to quantify the magnitude of a relationship between two or more variables . For example, in the fields of pharmacology and toxicology, scientific experiments are used to determine the dose-response relationship of a new drug or chemical. In these approaches, researchers perform a series of experiments in which a population of organisms , such as laboratory mice, is separated into groups and each group is exposed to a different amount of the drug or chemical of interest. The analysis of the data that result from these experiments (see our Data Analysis and Interpretation module) involves comparing the degree of the organism's response to the dose of the substance administered.

In this context, experiments can provide additional evidence to complement other research methods . For example, in the 1950s a great debate ensued over whether or not the chemicals in cigarette smoke cause cancer. Several researchers had conducted comparative studies (see our Comparison in Scientific Research module) that indicated that patients who smoked had a higher probability of developing lung cancer when compared to nonsmokers. Comparative studies differ slightly from experimental methods in that you do not consciously manipulate a variable ; rather you observe differences between two or more groups depending on whether or not they fall into a treatment or control group. Cigarette companies and lobbyists criticized these studies, suggesting that the relationship between smoking and lung cancer was coincidental. Several researchers noted the need for a clear dose-response study; however, the difficulties in getting cigarette smoke into the lungs of laboratory animals prevented this research. In the mid-1950s, Ernest Wynder and colleagues had an ingenious idea: They condensed the chemicals from cigarette smoke into a liquid and applied this in various doses to the skin of groups of mice. The researchers published data from a dose-response experiment of the effect of tobacco smoke condensate on mice (Wynder et al., 1957).

As seen in Figure 3, the researchers found a positive relationship between the amount of condensate applied to the skin of mice and the number of cancers that developed. The graph shows the results of a study in which different groups of mice were exposed to increasing amounts of cigarette tar. The black dots indicate the percentage of each sample group of mice that developed cancer for a given amount cigarette smoke "condensate" applied to their skin. The vertical lines are error bars, showing the amount of uncertainty . The graph shows generally increasing cancer rates with greater exposure. This study was one of the first pieces of experimental evidence in the cigarette smoking debate , and it helped strengthen the case for cigarette smoke as the causative agent in lung cancer in smokers.

Figure 3: Percentage of mice with cancer versus the amount cigarette smoke "condensate" applied to their skin (source: Wynder et al., 1957).

Sometimes experimental approaches and other research methods are not clearly distinct, or scientists may even use multiple research approaches in combination. For example, at 1:52 a.m. EDT on July 4, 2005, scientists with the National Aeronautics and Space Administration (NASA) conducted a study in which a 370 kg spacecraft named Deep Impact was purposely slammed into passing comet Tempel 1. A nearby spacecraft observed the impact and radioed data back to Earth. The research was partially descriptive in that it documented the chemical composition of the comet, but it was also partly experimental in that the effect of slamming the Deep Impact probe into the comet on the volatilization of previously undetected compounds , such as water, was assessed (A'Hearn et al., 2005). It is particularly common that experimentation and description overlap: Another example is Jane Goodall 's research on the behavior of chimpanzees, which can be read in our Description in Scientific Research module.

• Limitations of experimental methods

Figure 4: An image of comet Tempel 1 67 seconds after collision with the Deep Impact impactor. Image credit: NASA/JPL-Caltech/UMD http://deepimpact.umd.edu/gallery/HRI_937_1.html

While scientific experiments provide invaluable data regarding causal relationships, they do have limitations. One criticism of experiments is that they do not necessarily represent real-world situations. In order to clearly identify the relationship between an independent variable and a dependent variable , experiments are designed so that many other contributing variables are fixed or eliminated. For example, in an experiment designed to quantify the effect of vitamin A dose on the metabolism of beta-carotene in humans, Shawna Lemke and colleagues had to precisely control the diet of their human volunteers (Lemke, Dueker et al. 2003). They asked their participants to limit their intake of foods rich in vitamin A and further asked that they maintain a precise log of all foods eaten for 1 week prior to their study. At the time of their study, they controlled their participants' diet by feeding them all the same meals, described in the methods section of their research article in this way:

Meals were controlled for time and content on the dose administration day. Lunch was served at 5.5 h postdosing and consisted of a frozen dinner (Enchiladas, Amy's Kitchen, Petaluma, CA), a blueberry bagel with jelly, 1 apple and 1 banana, and a large chocolate chunk cookie (Pepperidge Farm). Dinner was served 10.5 h post dose and consisted of a frozen dinner (Chinese Stir Fry, Amy's Kitchen) plus the bagel and fruit taken for lunch.

While this is an important aspect of making an experiment manageable and informative, it is often not representative of the real world, in which many variables may change at once, including the foods you eat. Still, experimental research is an excellent way of determining relationships between variables that can be later validated in real world settings through descriptive or comparative studies.

Design is critical to the success or failure of an experiment . Slight variations in the experimental set-up could strongly affect the outcome being measured. For example, during the 1950s, a number of experiments were conducted to evaluate the toxicity in mammals of the metal molybdenum, using rats as experimental subjects . Unexpectedly, these experiments seemed to indicate that the type of cage the rats were housed in affected the toxicity of molybdenum. In response, G. Brinkman and Russell Miller set up an experiment to investigate this observation (Brinkman & Miller, 1961). Brinkman and Miller fed two groups of rats a normal diet that was supplemented with 200 parts per million (ppm) of molybdenum. One group of rats was housed in galvanized steel (steel coated with zinc to reduce corrosion) cages and the second group was housed in stainless steel cages. Rats housed in the galvanized steel cages suffered more from molybdenum toxicity than the other group: They had higher concentrations of molybdenum in their livers and lower blood hemoglobin levels. It was then shown that when the rats chewed on their cages, those housed in the galvanized metal cages absorbed zinc plated onto the metal bars, and zinc is now known to affect the toxicity of molybdenum. In order to control for zinc exposure, then, stainless steel cages needed to be used for all rats.

Scientists also have an obligation to adhere to ethical limits in designing and conducting experiments . During World War II, doctors working in Nazi Germany conducted many heinous experiments using human subjects . Among them was an experiment meant to identify effective treatments for hypothermia in humans, in which concentration camp prisoners were forced to sit in ice water or left naked outdoors in freezing temperatures and then re-warmed by various means. Many of the exposed victims froze to death or suffered permanent injuries. As a result of the Nazi experiments and other unethical research , strict scientific ethical standards have been adopted by the United States and other governments, and by the scientific community at large. Among other things, ethical standards (see our Scientific Ethics module) require that the benefits of research outweigh the risks to human subjects, and those who participate do so voluntarily and only after they have been made fully aware of all the risks posed by the research. These guidelines have far-reaching effects: While the clearest indication of causation in the cigarette smoke and lung cancer debate would have been to design an experiment in which one group of people was asked to take up smoking and another group was asked to refrain from smoking, it would be highly unethical for a scientist to purposefully expose a group of healthy people to a suspected cancer causing agent. As an alternative, comparative studies (see our Comparison in Scientific Research module) were initiated in humans, and experimental studies focused on animal subjects. The combination of these and other studies provided even stronger evidence of the link between smoking and lung cancer than either one method alone would have.

• Experimentation in modern practice

Like all scientific research , the results of experiments are shared with the scientific community, are built upon, and inspire additional experiments and research. For example, once Alhazen established that light given off by objects enters the human eye, the natural question that was asked was "What is the nature of light that enters the human eye?" Two common theories about the nature of light were debated for many years. Sir Isaac Newton was among the principal proponents of a theory suggesting that light was made of small particles . The English naturalist Robert Hooke (who held the interesting title of Curator of Experiments at the Royal Society of London) supported a different theory stating that light was a type of wave, like sound waves . In 1801, Thomas Young conducted a now classic scientific experiment that helped resolve this controversy . Young, like Alhazen, worked in a darkened room and allowed light to enter only through a small hole in a window shade. Young refocused the beam of light with mirrors and split the beam with a paper-thin card. The split light beams were then projected onto a screen, and formed an alternating light and dark banding pattern (Figure 5) – that was a sign that light was indeed a wave (see our Light I: Particle or Wave? module).

Figure 5: Young's depiction of the results of his experiment (Young, 1845). The dark spot represents the card held in front of a window slit, producing two parallel beams of light. The light and dark bands represent the brighter and darker bands he observed.

Approximately 100 years later, in 1905, new experiments led Albert Einstein to conclude that light exhibits properties of both waves and particles . Einstein's dual wave-particle theory is now generally accepted by scientists.

Experiments continue to help refine our understanding of light even today. In addition to his wave-particle theory , Einstein also proposed that the speed of light was unchanging and absolute. Yet in 1998 a group of scientists led by Lene Hau showed that light could be slowed from its normal speed of 3 x 10 8 meters per second to a mere 17 meters per second with a special experimental apparatus (Hau et al., 1999). The series of experiments that began with Alhazen 's work 1000 years ago has led to a progressively deeper understanding of the nature of light. Although the tools with which scientists conduct experiments may have become more complex, the principles behind controlled experiments are remarkably similar to those used by Pasteur and Alhazen hundreds of years ago.

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How to Measure and Improve Lab Accuracy and Precision

Accuracy (closeness to true value) and precision (consistency of measurements) are vital in scientific experiments. To improve these in the lab, regularly calibrate and maintain equipment, use tools within their appropriate ranges, record significant figures correctly, and take multiple measurements. Be aware of shifts over time and the impact of human variability. Advanced techniques like Measurement Systems Analysis (MSA) can further assess measurement reliability. Implementing these practices helps ensure data validity and sound experimental conclusions.

last updated: August 16, 2024

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One of the first things any scientist should consider when measuring any attribute, whether it is the concentration of a solution, the quantity of DNA in a sample, or fluorescence intensity, is how accurate is this measurement?

Understanding accuracy (and limitations) in the lab is of the utmost importance to forming sound conclusions about experiments. This article discusses accuracy and precision and provides concrete examples of ways to understand method limitations and improve measurements in your lab

What Do We Mean by Accuracy and Precision?

You likely have a good understanding of the difference between accuracy and precision . Since accuracy and precision are fundamental to almost all the sciences, they are typically one of the first subjects covered in introductory STEM courses. With that said, let’s get a quick refresher before we proceed to the consequences of misunderstanding accuracy and precision in the lab.

For every measurement we make, there is an actual value that we are trying to obtain. Furthermore, whenever we prepare a material by weighing or dispensing it, there is a target value we are trying to reach. Simply put, accuracy is how close a measurement is to the actual true value, whereas precision is how close those measurements tend to be to each other.

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For example, consider that you have four pipettes that you are using to dispense 30 µL of water. If you took 10 measurements from each pipette and knew the actual volume dispensed, you could determine whether each pipette was accurate and precise (see Figure 1 for an example).

Let’s analyze the chart in Figure 1 more closely. • Pipette 1 definitely outperforms the other three in terms of accuracy and precision —it most consistently dispenses volumes closest to our 30 µL target. We’ll give a gold star to this pipette. • Pipette 2 is relatively accurate compared to pipettes 3 and 4, but it’s not nearly as precise as pipette 1. That is, it does not consistently pipette similar volumes when used multiple times and has greater variability. • Pipette 3 is extremely precise, and pipettes have nearly identical volumes each time it is used. However, it’s not anywhere near the target! Therefore, it has lower accuracy than pipettes 1 or 2. • Finally, pipette 4 is the least accurate and precise of the bunch. Not only are the volumes dispensed all over the place (low precision), but they’re often nowhere near our target volume ( not accurate)! This is a pipette in need of some serious TLC.

For more on this topic, check out our article on checking pipette accuracy .

Why Is It Important to Be Aware of the Trueness and Precision of Our Measurements?

Understanding the concepts of accuracy (also known as trueness) and precision in a simple pipetting example is one matter, but it’s trickier to identify and monitor ALL the factors that may affect accuracy and precision, as well as the resulting impacts on your results.

Consider a situation where you are preparing custom cell culture media.

Perhaps you plan to weigh out 200 g of dextrose for your weekly experiments. You might go ahead and add dextrose with a scoop until the scale reads 200.0 g. How often do you think about how close the scale is reporting to the TRUE mass? It could be the case that the scale is out of calibration, and the actual mass you are adding is closer to 196 g. In other words, how accurate is your measurement?

Let’s assume that your scale is accurate. You still must consider the precision of the measurements! What if the scale is on a wobbly table, or a vent that turns on and off affects the measurement? In this case, the finished culture might turn out slightly differently from week to week.

Although this is a simple example, the effects can be pretty profound. In our example above, the composition of cell culture media directly affects the health and growth of cultures, which further influences other measurements we are taking and could change conclusions drawn from the entire experiment.

Simple issues like these can quickly compound and cascade into a plethora of issues , like increased variability and invalid results. Therefore, determining if your system measures a characteristic without bias and repeatably is clearly of crucial importance.

Is Any Measurement Ever Truly 100% Accurate and Precise?

Here’s the bad news—while being 100% accurate and precise is clearly ideal, it is impossible in reality. There is always some non-zero variability from factors outside our control, such as the instruments, environmental conditions, and lab personnel.

With that said, there are many things we can do to maximize accuracy and precision when conducting experiments. Drum roll, please…!

8 Ways to Improve Your Accuracy and Precision in the Lab

1. keep everything calibrated.

Calibration is the number one item on this list for a very important reason: it is the MOST critical means of ensuring your data and measurements are accurate.

Calibration involves adjusting or standardizing lab equipment so that it is more accurate AND precise.

Calibration typically requires comparing a standard to what your instrument is measuring and adjusting the instrument or software accordingly.

The complexity of calibrating instruments or equipment varies widely, but, typically, user manuals have recommended recalibration recommendations. Bitesize Bio has several articles on routine calibration, including routine calibration of pipettes and calibrating your lab scales .

2. Conduct Routine Maintenance

Even if all instruments in your lab are calibrated , odds are they need regular care to operate at their maximum accuracy and precision.

For instance, pH meters need routine maintenance that can be performed by novice scientists, while more sensitive instrumentation may require shipment of parts to vendors or even on-site visits.

Again, check your user manuals and call equipment manufacturers to ensure you take appropriate measures to keep lab equipment running under conditions optimal for accuracy.

3. Operate in the Appropriate Range with Correct Parameters

Always use tools that are designed and calibrated to work in the range you are measuring or dispensing. For example, don’t try to measure OD 600 beyond an absorption of >1.0 since optical density (OD) readings this high are beyond the dynamic range of most spectrophotometers! If you are ever unsure about using an instrument to measure accurately at an extreme value, reach out to a trusted peer or mentor for advice.

What if you are choosing between two tools that are both calibrated for use at a given target? You might have two pipettes that are both designed to dispense 100 µL (e.g., 20–100 µL or 100–1000 µL pipettes). When in doubt, choose the tool with more precision —in this case, the 20–100 µL pipette.

Watch our on-demand webinar on improving your pipetting technique for more information.

4. Understand Significant Figures (and Record Them Correctly!)

The number of significant figures (“sig figs”) you use and record is critical. Specifically, sig figs provide the degree of uncertainty associated with values.

Keep sig figs consistent when measuring items repeatedly, and ensure the number of sig figs you are using is appropriate for each measurement.

5. Take Multiple Measurements

The more samples you take for a given attribute, the more precise the representation of your measurement. In situations where sampling is destructive, or you can’t take multiple measurements (e.g., growth rates in a culture), you can increase the number of replicates to compensate.

However, for measurements like OD readings or cell counting , it’s reasonably easy to measure multiple parts of a single sample.

6. Detect Shifts Over Time

Some systems are prone to drift over time. For instance, background absorption in high-performance liquid chromatography (HPLC) may be indicative of column failure.

If you notice that measurements drift in a single direction over weeks or months, address the issue immediately by recalibration or preventative maintenance.

7. Consider the “Human Factor”

We don’t often talk about how a technique in the lab may vary from person to person, resulting in differences in the measurements of a single property.

To minimize the inherent variability between scientists, ensure that procedures are kept up to date and are as descriptive as possible.

In some cases, it may be easiest to have only one person responsible for a given measurement, but this may not always be possible. Ensure that all lab personnel are trained, especially on highly manual techniques like pipetting, to maximize accuracy and precision.

8. Perform a Measurement Systems Analysis (MSA)

While this is a relatively complicated method to gauge accuracy and precision, measurement systems analysis (or gage repeatability and reproducibility analysis) is the most comprehensive and statistically sound way to get a complete picture of the accuracy and precision of your measurement. This technique mathematically determines the amount of variation that exists when taking measurements multiple times.

To conduct an MSA, you’ll need to design a study that incorporates known and unknown sources of variation. There are various analysis methods available, but if your measurement is absolutely critical, it may be worth exploring. Stay tuned for a future article explaining various ways to conduct an MSA!

Final Thoughts

There are a wealth of resources on these topics if you want to learn more. For a more statistics-based primer on accuracy, precision, and trueness, check out Artel’s resource library on these topics . You might also consider reading about accuracy and precision through the International Organization of Standardization , which is a global organization that works to align scientists and engineers in every field when it comes to these topics.

Do you have more ideas on how to keep your lab measurements accurate and precise? Let us know in the comments below!

Q: What specific procedures or guidelines should be followed for the calibration of complex instruments like HPLC or mass spectrometers? A: Calibration procedures for complex instruments like HPLC or mass spectrometers typically involve using known standards to ensure that the instrument’s readings are accurate. For HPLC, this involves running calibration standards through the system to check for consistency in retention times and peak areas. Mass spectrometers often require calibration with specific calibration mixtures or reference gases. Follow manufacturer guidelines and industry standards, and document all your calibration activities. Regular calibration checks should be part of a routine schedule in your lab, with adjustments made based on the instrument’s usage and performance. Q: How can I effectively troubleshoot and address common issues that arise during calibration or maintenance, such as unexpected equipment behavior or inconsistent results? A: Encountering issues during calibration or maintenance is frustrating. Verify that the calibration standards and materials are correct and have not degraded. Check for any obvious physical issues, such as leaks, blockages, or component damage. Rebooting the instrument or resetting to factory settings can sometimes resolve unexpected behavior. If inconsistent results persist, recheck the calibration procedure, ensure the environment is stable (e.g., temperature, humidity), and consult the user manual or technical support. Documenting the troubleshooting steps will help identify patterns and prevent future occurrences. Q: What are some practical examples of Measurement Systems Analysis (MSA) being applied in real lab scenarios, and how can these methods be adapted to different types of experiments? A: MSA might be used in quality control environments to ensure that different operators and equipment produce consistent results. For example, in a pharmaceutical lab, MSA could be applied to assess the variability in tablet weight measurements across different scales and operators. This could involve conducting a Gage R&R study, where multiple operators measure the same sample using different instruments to assess repeatability and reproducibility. To adapt MSA to different experiments, tailor your analysis to the specific measurement type, whether it’s physical properties, chemical composition, or biological assays, and consider the potential sources of variation unique to that experiment.

Originally published October 11, 2021. Reviewed and updated August 2024

I am a results-oriented biochemist with over a decade of experience performing research and process development spanning microbiology, protein chemistry, and formulation development. My background includes extensive work in high-throughput assays, analytical chemistry, microbiology, project coordination, and lab management.

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How the Scientific Method Works

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Limitations of the Scientific Method

Clearly, the scientific method is a powerful tool, but it does have its limitations. These limitations are based on the fact that a hypothesis must be testable and falsifiable and that experiments and observations be repeatable. This places certain topics beyond the reach of the scientific method.

Science cannot prove or refute the existence of God or any other supernatural entity. Sometimes, scientific principles are used to try to lend credibility to certain nonscientific ideas, such as intelligent design . Intelligent design is the assertion that certain aspects of the origin of the universe and life can be explained only in the context of an intelligent, divine power. Proponents of intelligent design try to pass this concept off as a scientific theory to make it more palatable to developers of public school curriculums. But intelligent design is not science because the existence of a divine being cannot be tested with an experiment.

Science is also incapable of making value judgments. It cannot say global warming is bad, for example. It can study the causes and effects of global warming and report on those results, but it cannot assert that driving SUVs is wrong or that people who haven't replaced their regular light bulbs with LED bulbs are irresponsible.

Occasionally, certain organizations use scientific data to advance their causes. This blurs the line between science and morality and encourages the creation of "pseudo-science," which tries to legitimize a product or idea with a claim that has not been subjected to rigorous testing.

And yet, used properly, the scientific method is one of the most valuable tools humans have ever created. It helps us solve everyday problems around the house and, at the same time, helps us understand profound questions about the world and universe in which we live.

Most of the time, two competing theories can't exist to describe one phenomenon. But in the case of light , one theory is not enough. Many experiments support the notion that light behaves like a longitudinal wave. Taken collectively, these experiments have given rise to the wave theory of light. Other experiments, however, support the notion that light behaves as a particle. Instead of throwing out one theory and keeping the other, physicists maintain a wave/particle duality to describe the behavior of light.

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More Great Links

• Science Fair Project Resource Guide
• Understanding and Using the Scientific Method
• The Scientific Method
• Audubon, John James. "John James Audubon: Writings and Drawings." Library of America, 1999.
• Campbell, Neil A. and Jane B. Reece. "Biology, Seventh Edition." Pearson Benjamin Cummings, San Francisco, 2005.
• D'Agnese, Joseph. "Scientific Method Man." Wired, September 2004. http://www.wired.com/wired/archive/12.09/rugg.html.
• Introduction to the Scientific Method on Web Site of Frank Wolfs, Department of Physics and Astronomy, University of Rochester. http://teacher.pas.rochester.edu/phy_labs/AppendixE/AppendixE.html
• Keeton, William T. "Biological Science, Third Edition." W.W. Norton & Company, New York, 1980.
• The New Oxford American Dictionary. Oxford University Press, Oxford, United Kingdom. 2001.
• Understanding and Using the Scientific Method on Fact Monster. http://www.factmonster.com/cig/science-fair-projects/understanding-using-scientific-method.html
• Vecchione, Glen. "100 Amazing Award-Winning Science Fair Projects." Sterling Publishing Co., New York, 2001.
• Vecchione, Glen. "100 Amazing First Prize Science Fair Projects." Sterling Publishing Co., New York, 1998.

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